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WO2023029407A1 - 用于车辆的向紧急呼叫中心发送信息的方法及装置 - Google Patents

用于车辆的向紧急呼叫中心发送信息的方法及装置 Download PDF

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Publication number
WO2023029407A1
WO2023029407A1 PCT/CN2022/078010 CN2022078010W WO2023029407A1 WO 2023029407 A1 WO2023029407 A1 WO 2023029407A1 CN 2022078010 W CN2022078010 W CN 2022078010W WO 2023029407 A1 WO2023029407 A1 WO 2023029407A1
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WIPO (PCT)
Prior art keywords
bleeding
occupant
detection
cabin
image information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2022/078010
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English (en)
French (fr)
Inventor
邵昌旭
许亮
李轲
王飞
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Sensetime Intelligent Technology Co Ltd
Original Assignee
Shanghai Sensetime Intelligent Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Shanghai Sensetime Intelligent Technology Co Ltd filed Critical Shanghai Sensetime Intelligent Technology Co Ltd
Priority to KR1020247009195A priority Critical patent/KR20240046910A/ko
Priority to JP2024513513A priority patent/JP7734272B2/ja
Publication of WO2023029407A1 publication Critical patent/WO2023029407A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
    • G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
    • G08B25/10—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb using a particular sensing technique
    • A61B5/1128—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb using a particular sensing technique using image analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4504—Bones
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient; User input means
    • A61B5/7465—Arrangements for interactive communication between patient and care services, e.g. by using a telephone network
    • A61B5/747—Arrangements for interactive communication between patient and care services, e.g. by using a telephone network in case of emergency, i.e. alerting emergency services
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/42—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
    • G06V10/422—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation for representing the structure of the pattern or shape of an object therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/90—Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G06T2207/30104—Vascular flow; Blood flow; Perfusion

Definitions

  • the present disclosure relates to the field of computer technology, and in particular to a method and device for sending information to an emergency call center for vehicles, electronic equipment, computer program products and storage media.
  • an on-board emergency call (eCall) system can be integrated on the car.
  • the eCall system is a typical application of the Internet of Vehicles. Based on technologies such as vehicle sensing, mobile communication, and satellite positioning, it can contact the public rescue center as soon as the accident occurs, and automatically send the vehicle location and vehicle information to the rescue center, and the rescue center will rescue the accident personnel after confirming the accident .
  • the present disclosure proposes a technical solution for information sending.
  • a vehicle of sending information to an emergency call center comprising:
  • the bleeding condition is sent to an emergency call center.
  • the detecting the bleeding of the occupants in the cabin based on the image information includes:
  • a blood test is performed on the occupant's face and/or body surface to determine the bleeding condition of the occupant in the cabin.
  • the detecting the bleeding of the occupants in the cabin based on the image information includes:
  • the detecting the bleeding of the occupants in the cabin based on the image information includes:
  • the bleeding condition of the occupant is determined based on the area detection results of each of the detection areas.
  • the detecting the body surface area of the occupant in the cabin based on the image information includes:
  • Said dividing the occupant's body surface area into a plurality of detection areas includes:
  • the face surface area of the occupant is divided into a plurality of detection areas.
  • the detecting blood information in each of the detection areas, and obtaining the area detection results of each of the detection areas includes:
  • the confidence levels of the plurality of first detection areas and the second detection area are increased to second confidence level
  • the determining the bleeding condition of the occupant based on the detection results of each detection region includes:
  • the severity of bleeding is determined, and the severity of bleeding is positively correlated with the sum of the areas of blood flow in each of the detection regions.
  • the detecting the bleeding of the occupants in the cabin based on the image information includes:
  • the body part where the blood flow starts is taken as the bleeding part.
  • the method further includes:
  • the body posture is a preset abnormal body posture, and the duration of the abnormal body posture exceeds a set duration, it is determined that the body posture of the occupant in the cabin is an abnormal body posture.
  • the method further includes:
  • the body posture of the occupant in the cabin is a preset fracture posture, it is determined that the occupant in the cabin has a fracture condition.
  • the method further includes:
  • the vital sign index includes at least one of the following:
  • the vital sign indicator is sent to an emergency call center.
  • the method further includes:
  • the injury severity level is sent to an emergency call center.
  • an apparatus for a vehicle for sending information to an emergency call center comprising:
  • an image information acquisition unit configured to acquire image information of occupants in the cabin in response to an emergency call being triggered
  • a bleeding condition detection unit configured to detect the bleeding condition of the occupants in the cabin based on the image information
  • a bleeding situation sending unit configured to send the bleeding situation to an emergency call center in response to detecting the bleeding situation.
  • the bleeding situation detection unit includes:
  • the occupant detection subunit is used to perform face detection and/or human body detection on the image information to determine the occupants in the cabin;
  • the first bleeding condition determining subunit is configured to perform blood detection on the occupant's face and/or body surface to determine the bleeding condition of the occupant in the cabin.
  • the bleeding condition detection unit is configured to detect whether the occupant is bleeding based on the image information based on blood color information and blood flow shape information.
  • the bleeding situation detection unit includes:
  • a body surface area detection subunit configured to detect the body surface area of the occupant in the cabin based on the image information
  • a detection area division subunit configured to divide the occupant's body surface area into a plurality of detection areas
  • An area detection result determining subunit configured to detect blood information in each of the detection areas, and obtain the area detection results of each of the detection areas;
  • the second bleeding condition determining subunit is configured to determine the bleeding condition of the occupant based on the area detection results of each of the detection areas.
  • the body surface region detection subunit is configured to detect the face surface region of an occupant in the cabin based on the image information
  • the detection area division subunit is configured to divide the occupant's face surface area into a plurality of detection areas.
  • the region detection result determination subunit is configured to determine a first confidence level of bleeding in each detection region based on the shape and area of blood flow in each detection region; determine Whether there is a contiguous blood flow between adjacent detection areas; in response to determining that there is a contiguous blood flow between the first detection area and the adjacent second detection area in the detection areas, the plurality of first detection areas The confidence level of the first detection area and the second detection area is increased to a second confidence level;
  • the second bleeding condition determination subunit is configured to determine that the occupant is bleeding when the first confidence level or the second confidence level exceeds a confidence threshold; based on the blood in each detection area The area of the flow determines the severity of the bleeding, and the severity of the bleeding is positively correlated with the sum of the areas of the blood flow in each of the detection regions.
  • the bleeding situation detection unit includes:
  • a bleeding part detection subunit configured to determine a bleeding body part and a direction of blood flow in response to detecting that the occupant in the cabin is bleeding based on the image information
  • the bleeding site detection subunit is configured to use the body site where the blood flow starts as the bleeding site based on the bleeding body site and the direction of the blood flow.
  • the device further includes:
  • a body posture determining unit configured to determine the body posture of the occupant in the cabin according to the image information
  • An abnormal body posture determination unit configured to determine that the body posture of the occupant in the cabin is an abnormal body posture when the body posture is a preset abnormal body posture and the duration of the abnormal body posture exceeds a set duration. attitude.
  • the device further includes:
  • the fracture condition detection unit is configured to determine that the occupant in the cabin has a fracture condition when it is determined that the body posture of the occupant in the cabin is a preset fracture posture.
  • the device further includes:
  • a vital sign indicator determining unit configured to determine the vital sign indicator of the occupant based on the image information, and the vital sign indicator includes at least one of the following:
  • the vital sign indicator sending unit is configured to send the vital sign indicator to an emergency call center.
  • the device further includes:
  • the injury severity determination unit is configured to determine the injury severity of the occupant in the cabin based on at least one of the determined bleeding condition, abnormal body posture, and vital signs of the occupant;
  • the injury severity sending unit is configured to send the injury severity to an emergency call center.
  • an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the above-mentioned method.
  • a computer-readable storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above method is implemented.
  • a computer program product including computer readable codes, or a non-volatile computer readable storage medium bearing computer readable codes, when the computer readable codes are stored in an electronic device
  • the processor in the electronic device is used to implement the above method.
  • image information of occupants in the cabin is acquired in response to an emergency call being triggered, and based on the image information, bleeding conditions of the occupants in the cabin are detected, and then in response to the detection of bleeding conditions, the bleeding The situation is sent to the emergency call center.
  • the emergency call center can reasonably dispatch the rescue force according to the injuries of the occupants in the accident, and shorten or omit the inquiry process of the call center personnel for the scene of serious bleeding, and race against time. rescue.
  • FIG. 1 shows a flowchart of an information sending method according to an embodiment of the present disclosure
  • Fig. 2 shows a block diagram of an information sending device according to an embodiment of the present disclosure
  • Fig. 3 shows a block diagram of an electronic device according to an embodiment of the present disclosure
  • Fig. 4 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • the emergency call service can shorten the rescue time and reduce the death rate of rescued persons in vehicle accidents.
  • it is difficult to judge the degree of accident damage after the accident, and it is impossible to confirm the casualties of the people in the vehicle.
  • the rescue center cannot reasonably dispatch rescue forces.
  • image information of occupants in the cabin is acquired in response to an emergency call being triggered, and based on the image information, bleeding conditions of the occupants in the cabin are detected, and then in response to the detection of bleeding conditions, the bleeding The situation is sent to the emergency call center.
  • the emergency call center can reasonably dispatch the rescue force according to the injuries of the occupants in the accident, and shorten or omit the inquiry process of the call center personnel for the scene of serious bleeding. rescue.
  • the execution subject of the method may be an intelligent driving control device installed on the vehicle.
  • the method may be executed by a terminal device or a server or other processing device.
  • the terminal device may be a vehicle-mounted device, a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, or a Wearable equipment etc.
  • UE user equipment
  • PDA Personal Digital Assistant
  • the on-board equipment can be the car machine or domain controller in the cabin, or ADAS (Advanced Driving Assistance System, advanced driver assistance system), OMS (Occupant Monitoring System, occupant monitoring system) or DMS (Driver Monitoring System, A device host, etc. used to execute an information sending method in a driver monitoring system).
  • the information sending method may be implemented by a processor invoking computer-readable instructions stored in a memory.
  • the subject of execution of the steps of the sending method may be executed by hardware, or executed by a processor running computer executable codes.
  • Fig. 1 shows a flowchart of a method for a vehicle to send information to an emergency call center according to an embodiment of the present disclosure.
  • the method for a vehicle to send information to an emergency call center includes:
  • step S11 in response to the emergency call being triggered, image information of occupants in the cabin is acquired;
  • the image information here is the image information of the occupants in the vehicle cabin.
  • the vehicle here can be at least one of the types of vehicles such as private cars, shared cars, online car-hailing, taxis, and trucks.
  • the specific types of vehicles in this disclosure Not limited.
  • the image information here can be the image information of the area where the passengers in the cabin are located, and the image information can be collected by a vehicle-mounted image acquisition device installed inside or outside the cabin of the vehicle.
  • the vehicle-mounted image acquisition device can be a vehicle-mounted camera or is equipped with a camera image capture device.
  • the camera can be a camera for collecting image information inside the vehicle, or a camera for collecting image information outside the vehicle.
  • the camera may include a camera in the DMS and/or a camera in the OMS, etc., and these cameras may be used to collect image information inside the vehicle; the camera may also include a camera in the ADAS, which may be used to collect image information outside the vehicle .
  • the vehicle-mounted image acquisition device may also be a camera in other systems, or may also be a separately configured camera, and the embodiment of the present disclosure does not limit the specific vehicle-mounted image acquisition device.
  • the carrier of the image information here can be a two-dimensional image or video, for example, the image information can specifically be a visible light image/video, or an infrared image/video; it can also be a three-dimensional image composed of a point cloud scanned by radar, etc., specifically It may be determined according to the actual application scenario, which is not limited in the present disclosure.
  • the image information collected by it can be obtained through the communication connection established with the vehicle-mounted image collection device.
  • the vehicle-mounted image acquisition device can transmit the collected image information to the vehicle-mounted controller or remote server through the bus or wireless communication channel in real time, and the vehicle-mounted controller or remote server can receive real-time image information through the bus or wireless communication channel .
  • step S12 based on the image information, the bleeding of the occupants in the cabin is detected
  • Whether there is blood on the occupant's body can be detected based on image processing technology to determine whether the occupant is bleeding.
  • a neural network can be used to detect occupant bleeding, or target detection techniques such as threshold segmentation can be used to detect blood in an image to detect occupant bleeding in the cabin.
  • target detection techniques such as threshold segmentation can be used to detect blood in an image to detect occupant bleeding in the cabin.
  • step S13 in response to detecting a bleeding condition, the bleeding condition is sent to an emergency call center.
  • the blood flow situation sent to the emergency call center for example, whether there is a blood flow situation or no blood flow situation for the occupants in the cabin; or, when there is a blood flow situation, a more specific The bleeding situation, for example, where the bleeding occurred, the severity of the bleeding, and so on.
  • the emergency call center can determine whether the emergency call originator has bleeding, and provide targeted rescue measures when it is determined that there is bleeding, such as carrying corresponding hemostatic supplies , blood transfusion supplies, additional doctors to deal with bleeding situations, etc.
  • image information of occupants in the cabin is acquired in response to an emergency call being triggered, and based on the image information, bleeding conditions of the occupants in the cabin are detected, and then in response to the detection of bleeding conditions, the bleeding The situation is sent to the emergency call center.
  • the emergency call center can reasonably dispatch the rescue force according to the injuries of the occupants in the accident, and shorten or omit the inquiry process of the call center personnel for the scene of serious bleeding, and race against time. rescue.
  • the detecting the bleeding of the occupants in the cabin based on the image information includes: performing face detection and/or human body detection on the image information to determine the occupants in the cabin ; Carrying out a blood test on the face and/or body surface of the occupant to determine the bleeding situation of the occupant in the cabin.
  • human body detection and/or face detection can be performed in the cabin based on the image information, and the human body detection result and/or face detection result in the cabin can be obtained, and can be based on the cabin
  • the human body detection result and/or face detection result in the cabin is used to obtain the occupant detection result in the cabin.
  • the human body detection result and/or human face detection result in the cabin may be used as the occupant detection result in the cabin.
  • the human body detection result and/or face detection result in the cabin may be processed to obtain the occupant detection result in the cabin.
  • human body detection and/or face detection are performed in the cabin to obtain human body detection results and/or face detection results in the cabin, and the detection results include human body and/or face detection results. location information. For example, when one occupant is detected, the occupant detection result includes the occupant's position information; when multiple occupants are detected, the occupant detection result may include the detected occupant's position information.
  • the position information of the occupant may be represented by position information of a bounding box of the occupant. Then, in the image framed by the bounding box, blood detection is performed on the occupant.
  • the position information of the occupant may be represented by the position information of the boundary contour of the occupant, and then blood detection is performed on the occupant in the image surrounded by the boundary contour.
  • the position of the occupant's face can be obtained.
  • blood detection can be performed on the occupant's face to determine the bleeding situation of the occupant in the cabin;
  • human body detection the position of the occupant's body can be obtained.
  • a blood test can be performed on the body surface of the occupant to determine the bleeding of the occupant in the cabin.
  • the occupant in the cabin is determined by performing face detection and/or human body detection on the image information, and then blood detection is performed on the occupant's face and/or body surface to determine the occupant in the cabin.
  • the bleeding of the occupants is performed on the image information first, and blood detection is performed on the face and/or body surface of the occupant, which can not only reduce the image range for subsequent blood detection, improve detection efficiency, but also reduce The interference of other areas of the occupant's body on blood testing improves the accuracy of bleeding detection.
  • the specific implementation of blood detection can be based on the color information of blood and the shape information of blood flow. Then, in a possible implementation, the detection of the bleeding of the occupants in the cabin based on the image information, The method includes: based on the color information of the blood and the shape information of the blood flow, detecting whether the occupant is bleeding based on the image information.
  • the color of the blood is often bright red.
  • the expression of the color is realized by defining the color parameters in the color space.
  • the commonly used color space is the red-green-blue (RGB) color space.
  • RGB red-green-blue
  • CIE International Commission on Illumination
  • the corresponding color parameters can be analyzed from the image information.
  • the image When the image is stored in the computer, it will be stored in the default color space.
  • the default color space of most images in the computer is RGB color space.
  • the RGB color space is divided into three color components: red (R), green (G), and blue (B).
  • the value range of each color component is 0 to 255.
  • the computer reads the image information from the storage medium, it reads the image information through digital image processing technology, that is, it can obtain the three-dimensional component of each pixel in the image information in the default color space, that is, obtain Specifies the color parameters of the image information in the default color space.
  • Each pixel has a color parameter, and different color parameters represent different colors. Therefore, the color of the pixel can be determined based on the color parameter.
  • its color parameter can be a range.
  • the range of blood color parameter can be expressed as (150,0,0)-(250,50,50) , when the color range of the pixel is within this range, it can be considered as the color of blood, and further, verification is performed according to the shape of the blood.
  • the detection of bleeding can also be realized based on neural network technology.
  • image features are often extracted through image convolution operations. Then, the color information of the blood will be extracted as a high-dimensional feature representation, and the neural network can determine the high-dimensional features belonging to the blood color based on the high-dimensional features, and then detect bleeding.
  • the color of the occupant's clothing, accessories and other attachments may be similar to the color of blood, it can be further determined based on the shape of the blood flow to determine whether the occupant is bleeding.
  • the shape of the blood flow can be based on the shape of the real blood flow in the image get.
  • Blood detection can be implemented based on a deep neural network, and the neural network used for blood detection can be, for example, a Seq2Seq model based on an attention mechanism, a Tensorflow model, and the like.
  • the neural network can be a well-trained network, or according to the characteristics of the occupant bleeding image information, the neural network can be trained by using the image data set containing the occupant bleeding situation in the image content, and by marking the bleeding area in the image data set, the The neural network is trained so that the neural network has a high accuracy rate when detecting blood, and can accurately detect the blood on the occupant's face and/or body surface.
  • the bleeding of the occupant in the cabin can be accurately detected.
  • the detecting the bleeding of the occupant in the cabin based on the image information includes: detecting the body surface area of the occupant in the cabin based on the image information; The body surface area is divided into a plurality of detection areas; blood information is detected in each of the detection areas to obtain the area detection results of each of the detection areas; based on the area detection results of each of the detection areas, the occupant's bleeding is determined Condition.
  • the body surface area of the occupant in the cabin can be detected, the body surface area can be represented based on the coordinates in the image, and the body surface area can be divided into multiple detection areas.
  • the body surface area can be divided into grids, which can divide the exposed parts of the body (such as people's faces, necks, etc.) into grids with the same area;
  • the body surface area can also be divided according to the body parts in the body surface, the arm of the body surface can be divided into a detection area, the chest can be divided into a detection area, the abdomen can be divided into a detection area, and so on.
  • the key points of the human body can be detected to obtain the key points used to identify the body parts, and then the body surface area is divided according to the key points to obtain multiple detection areas.
  • the body surface area is divided according to the body parts, which is not limited in the present disclosure.
  • blood information can be detected in each detection area.
  • the specific detection method of blood information can refer to the possible implementation methods provided in this disclosure.
  • blood can be detected according to blood color and blood flow shape, Or it may be to detect blood through a trained neural network, which will not be described here.
  • the area detection result of each detection area can be obtained.
  • the detection result of this area can be that there is bleeding or there is no bleeding. When there is bleeding, it can also be specific to the presence of bleeding. The specific location information of the blood flow in the detection area is detected.
  • the regional detection results of each detection region can be weighted and fused to determine the occupant's bleeding situation; or, the detection results of each region can be integrated to determine the confidence and severity of the occupant's bleeding.
  • the occupant's body surface area in the cabin is detected; the occupant's body surface area is divided into multiple detection areas; blood information is detected in each detection area to obtain the Area detection results, thus, according to the area detection results of each detection area, the occupant's bleeding condition can be determined, and the accuracy of the determined bleeding condition can be improved.
  • the detecting the body surface area of the occupant in the cabin based on the image information includes: detecting the face surface area of the occupant in the cabin based on the image information; Dividing the occupant's body surface area into multiple detection areas includes: dividing the occupant's face surface area into multiple detection areas.
  • the face surface area of the occupant in the cabin can be detected, the face surface area can be represented based on the coordinates in the image, and the face surface area can be divided into a plurality of detection areas.
  • the surface area of the human face can be divided into grids, and the grid can be a grid with the same area.
  • the face surface area can also be divided according to the parts in the face surface, the forehead can be divided into a detection area, the sides of the nose can be divided into a detection area, and the mouth and the area below Partially divided into a detection area, etc.
  • the face key point detection can be performed on the face surface to obtain the key points used to identify the face parts, and then The face surface area is divided according to the key point to obtain multiple detection areas.
  • there may be multiple ways of dividing the surface area of the human face which is not limited in the present disclosure.
  • the face surface area of the occupant in the cabin is detected, and then the face surface area of the occupant is divided into multiple detection areas, thus, according to the area detection results of each detection area, Determining the bleeding on the occupant's face can improve the accuracy of the determined bleeding.
  • the detecting the blood information in each of the detection areas and obtaining the area detection results of each of the detection areas includes: based on the shape and area of the blood flow in each detection area, determining A first degree of confidence that bleeding occurs in each of the detection areas; determine whether there is a contiguous blood flow between adjacent detection areas; There is a connected blood flow in the two detection areas, and the confidence level of the plurality of first detection areas and the second detection area is increased to the second confidence level; the area detection results based on each of the detection areas, Determining the occupant's bleeding condition includes: determining that the occupant is bleeding if the first confidence level or the second confidence level exceeds a confidence threshold; based on the blood flow in each detection area The area determines the severity of the bleeding, and the severity of the bleeding is positively correlated with the area sum of the blood flow in each of the detection areas.
  • detecting blood flow in image information based on image processing technology it can be regarded as a binary classification problem of determining whether the pixels in the image belong to blood flow, which can be realized based on image segmentation technology in deep learning, so that the detection area can be obtained
  • a plurality of pixel points classified as blood flow are connected to form a blood flow area, that is, a blood flow shape is formed.
  • the area of the blood flow may be the area of multiple pixel points classified as blood flow in the image, or it may also be converted into the real area of the occupant's body surface.
  • the first confidence level represents the degree of blood flow in a single detection area. Credibility.
  • the first confidence level may be positively correlated with the area of the blood flow in the area, that is, the larger the area, the higher the first confidence level; the closer the shape of the blood flow in the detection area is to the shape of the real blood flow, the higher the first confidence level .
  • each detection area After obtaining the first confidence level of each detection area, it can be further determined whether there is a contiguous blood flow between each detection area. Specifically, it can be determined according to the position of blood determined in each detection area. The blood flow in each detection area The position may be a position in the image information. If the positions of the blood are adjacent in the image information, it can be determined that there is a contiguous blood flow between the detection areas.
  • the first confidence levels of the first detection area and the second detection area are respectively 0.6 and 0.7.
  • the confidence levels of the second detection region are increased to 0.7 and 0.8 respectively, to obtain the second confidence level.
  • the specific increase range of the first confidence level can be determined according to actual needs, which is not specifically limited in the present disclosure.
  • a confidence threshold may be preset, and when the first confidence level or the second confidence level exceeds the confidence level threshold, it is determined that the occupant is bleeding.
  • the specific setting of the confidence threshold may be determined according to actual requirements, and this disclosure does not specifically limit it.
  • the severity of bleeding can be determined based on the area of blood flow in each detection area, and the degree of bleeding The degree of severity is positively correlated with the area sum of the blood flow in each detection area.
  • the first confidence level of bleeding in each detection area based on the shape and area of the blood flow in each detection area, determine the first confidence level of bleeding in each detection area; blood flow; in response to determining that there is blood flow in contact between a first detection area and an adjacent second detection area among the detection areas, the confidence levels of the plurality of first detection areas and the second detection area Raising to a second confidence level; determining that the occupant is bleeding if the first confidence level or the second confidence level exceeds a confidence level threshold.
  • the accuracy of the determined bleeding can be increased.
  • the detecting the bleeding of the occupant in the cabin based on the image information includes: in response to detecting the bleeding of the occupant in the cabin based on the image information, determining the bleeding body part and The direction of the blood flow: based on the bleeding body part and the direction of the blood flow, the body part where the blood flow starts is taken as the bleeding part.
  • the body part of the occupant's body surface can be determined, and thus the body part where the blood is located can be determined.
  • the blood may have crossed multiple body parts after bleeding from a certain part , thus, the direction of the blood flow can be further determined, and based on the direction of the blood flow, the part of the body where the starting end of the blood flow is located is taken as the bleeding site.
  • the direction of blood flow it can be determined based on multiple video frames in the image information, blood detection is performed in multiple video frames, and the direction of gradually increasing blood flow can be determined according to the blood flow in multiple video frames, This direction can then be regarded as the direction of blood flow.
  • the bleeding body part and the direction of the blood flow are determined; based on the bleeding body part and the direction of the blood flow, the blood The part of the body where the stream starts, as the bleeding site.
  • the bleeding site can be accurately determined, and the bleeding site can be sent to the emergency call center as the bleeding situation, so that the emergency call center can clarify the bleeding site of the caller, and the severity of the bleeding can be determined according to the bleeding site, and the rescue force can be reasonably dispatched, so that Provide targeted assistance.
  • the method further includes: determining the body posture of the occupant in the cabin according to the image information; when the body posture is a preset abnormal body posture and the abnormal body posture When the duration exceeds the set duration, it is determined that the body posture of the occupant in the cabin is an abnormal body posture.
  • the body posture of the occupant in the cabin can be determined based on the image information, for example, the body posture of the occupant can be determined through image recognition technology.
  • Body posture detection can be determined based on key detection of the human body.
  • multiple key points of the human body to be detected can be set in advance.
  • 17 key points can be set in the human body skeleton, indicating each part of the human body respectively. , by detecting these 17 key points, according to the positional relationship between these 17 key points, the positional relationship between the various parts of the human body can be obtained, and the positional relationship between the various parts of the human body is the specific form of body posture.
  • the image information can be input into the backbone network, feature extraction is performed on the image information via the backbone network to obtain a feature map, and then the position of the key points of the human body is detected based on the feature map to obtain the pose of the human body.
  • the backbone network may adopt network structures such as ResNet and MobileNet, which are not limited here.
  • the body posture of the occupant in the cabin After determining the body posture of the occupant in the cabin, it can be judged whether the body posture is a preset abnormal body posture. If the body posture is determined to be the preset abnormal body posture, it is determined that the health status of the occupant in the cabin is abnormal.
  • Preset abnormal body postures include at least one of the following: body tilted to one side, head tilted downward, face upward. Since the body posture of the occupant can reflect the health status of the user to a certain extent, when an accident occurs, the occupant is often unable to maintain a straight posture when the occupant is injured. Abnormal postures such as tilting down or facing up. These body gestures can accurately represent the user's current health status as abnormal.
  • these abnormal body postures can be set in advance. After determining the body posture of the occupant in the cabin, it can be judged whether the body state of the occupant in the cabin is a preset abnormal body posture. In the case of the body posture, it is determined that the health status of the occupant in the cabin is abnormal.
  • the body posture is a preset abnormal body posture and the duration of the abnormal body posture exceeds the set duration, it is determined that the body posture of the occupant in the cabin is Unusual body posture.
  • the body posture of the occupant in the cabin is determined according to the image information; when the body posture is a preset abnormal body posture and the duration of the abnormal body posture exceeds the set duration In some cases, it is determined that the body posture of the occupant in the cabin is an abnormal body posture.
  • the abnormal situation of the occupant can be accurately determined, so that the severity level of the occupant's injury can be accurately determined subsequently, and the severity level of the occupant's injury can be sent to the emergency call center, so that the emergency call center can give priority to rescuing seriously injured passengers.
  • the method further includes: in a case where it is determined that the body posture of the occupant in the cabin is a preset fracture posture, determining that the occupant in the cabin has a fracture condition.
  • the body posture of the occupant is obviously different from the normal body posture.
  • the non-joint parts of the occupant are bent, which can clearly indicate that the occupant has a fracture.
  • the method further includes: determining a vital sign index of the occupant based on the image information, where the vital sign index includes at least one of the following: respiratory rate, blood pressure, and heart rate; Vital sign indicators are sent to the emergency call center.
  • the vital sign index can be determined based on the physiological characteristic sensing information collected by the physiological characteristic sensor.
  • the vital sign sensor is a millimeter-wave radar as an example.
  • the frequency of the echo signal is used to detect the occupant's breathing and heart rate.
  • the vital sign sensing information is the echo signal of the millimeter-wave radar.
  • Millimeter-wave radar can detect tiny vibrations and displacements of the human body by measuring changes in the phase of echo signals.
  • the frequency of heartbeat and breathing can be determined based on the detection of chest vibration amplitude.
  • the vital sign index can be sent to the emergency call center, so that the emergency call center can reasonably dispatch rescue forces and carry out targeted rescue for passengers.
  • the method further includes: determining the injury severity level of the occupant in the cabin based on at least one of the determined bleeding condition, abnormal body posture, and vital sign index of the occupant; Injury severity levels are sent to emergency call centers.
  • the injury severity level is used to represent the severity of the occupant's injury, and the level can be divided into, for example, 0-10. The higher the level, the more serious the injury, and the level 0 indicates that the occupant is not injured.
  • one injury level can be used to comprehensively represent the severity of the occupant's bleeding, abnormal body posture, and vital sign indicators, or multiple severity levels can be used to represent the severity of the occupant's injury respectively.
  • bleeding severity level, fracture severity level, vital sign weakness level, etc. may be preset.
  • the severity level of bleeding can be determined according to the area of bleeding and the bleeding site.
  • the area of bleeding is positively correlated with the severity level of bleeding.
  • the severity of the fracture is positively correlated with the degree of bending of the human skeleton and the number of bones that are fractured. The higher the severity of the bleeding, the higher the fracture site is in the head and other vital parts.
  • the level of vital signs weakness is negatively correlated with the vital signs indicators. The lower the respiratory rate, blood pressure, and heart rate, the higher the level of vital signs weakness.
  • the weighted average of the severity levels of the occupant's bleeding situation, abnormal body posture, and vital signs indicators can be calculated.
  • a comprehensive injury severity level can be obtained; alternatively, the injury severity level can be determined according to the most serious of the occupant's bleeding, abnormal body posture, and vital sign indicators.
  • the injury severity level of the occupant in the cabin is determined, and the injury severity level is sent to the emergency call center.
  • the emergency call center can give priority to rescuing occupants of the calling party with higher injury severity according to the injury severity level, reduce or omit the inquiry process, provide faster rescue, and reduce personal and property losses.
  • the present disclosure also provides a device for sending information to an emergency call center, an electronic device, a computer-readable storage medium, and a program for a vehicle, all of which can be used to implement any information sending method provided by the present disclosure, and the corresponding technical solutions And description and refer to the corresponding record in the method part, no more details.
  • FIG. 2 shows a block diagram of an information sending device according to an embodiment of the present disclosure.
  • the device 20 includes:
  • An image information acquisition unit 21 configured to acquire image information of occupants in the cabin in response to the emergency call being triggered;
  • a bleeding condition detection unit 22 configured to detect the bleeding condition of the occupants in the cabin based on the image information
  • the bleeding situation sending unit 23 is configured to send the bleeding situation to an emergency call center in response to detecting the bleeding situation.
  • the bleeding situation detection unit includes:
  • the occupant detection subunit is used to perform face detection and/or human body detection on the image information to determine the occupants in the cabin;
  • the first bleeding condition determining subunit is configured to perform blood detection on the occupant's face and/or body surface to determine the bleeding condition of the occupant in the cabin.
  • the bleeding condition detection unit is configured to detect whether the occupant is bleeding based on the image information based on blood color information and blood flow shape information.
  • the bleeding situation detection unit includes:
  • a body surface area detection subunit configured to detect the body surface area of the occupant in the cabin based on the image information
  • a detection area division subunit configured to divide the occupant's body surface area into a plurality of detection areas
  • An area detection result determining subunit configured to detect blood information in each of the detection areas, and obtain the area detection results of each of the detection areas;
  • the second bleeding condition determining subunit is configured to determine the bleeding condition of the occupant based on the area detection results of each of the detection areas.
  • the body surface region detection subunit is configured to detect the face surface region of an occupant in the cabin based on the image information
  • the detection area division subunit is configured to divide the occupant's face surface area into a plurality of detection areas.
  • the region detection result determination subunit is configured to determine a first confidence level of bleeding in each detection region based on the shape and area of blood flow in each detection region; determine Whether there is a contiguous blood flow between adjacent detection areas; in response to determining that there is a contiguous blood flow between the first detection area and the adjacent second detection area in the detection areas, the plurality of first detection areas The confidence level of the first detection area and the second detection area is increased to a second confidence level;
  • the second bleeding condition determination subunit is configured to determine that the occupant is bleeding when the first confidence level or the second confidence level exceeds a confidence threshold; based on the blood in each detection area The area of the flow determines the severity of the bleeding, and the severity of the bleeding is positively correlated with the sum of the areas of the blood flow in each of the detection regions.
  • the bleeding situation detection unit includes:
  • a bleeding part detection subunit configured to determine the bleeding body part and the direction of the blood flow in response to detecting that the occupant in the cabin is bleeding based on the image information
  • the bleeding site detection subunit is configured to use the body site where the blood flow starts as the bleeding site based on the bleeding body site and the direction of the blood flow.
  • the device further includes:
  • a body posture determining unit configured to determine the body posture of the occupant in the cabin according to the image information
  • An abnormal body posture determination unit configured to determine that the body posture of the occupant in the cabin is an abnormal body posture when the body posture is a preset abnormal body posture and the duration of the abnormal body posture exceeds a set duration. attitude.
  • the device further includes:
  • the fracture condition detection unit is configured to determine that the occupant in the cabin has a fracture condition when it is determined that the body posture of the occupant in the cabin is a preset fracture posture.
  • the device further includes:
  • a vital sign indicator determining unit configured to determine the vital sign indicator of the occupant based on the image information, and the vital sign indicator includes at least one of the following:
  • the vital sign indicator sending unit is configured to send the vital sign indicator to an emergency call center.
  • the device further includes:
  • the injury severity determination unit is configured to determine the injury severity of the occupant in the cabin based on at least one of the determined bleeding condition, abnormal body posture, and vital signs of the occupant;
  • the injury severity sending unit is configured to send the injury severity to an emergency call center.
  • the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments, and its specific implementation and technical effects can refer to the descriptions of the above method embodiments, for It is concise and will not be repeated here.
  • Embodiments of the present disclosure also provide a computer-readable storage medium, on which computer program instructions are stored, and the above-mentioned method is implemented when the computer program instructions are executed by a processor.
  • Computer readable storage media may be volatile or nonvolatile computer readable storage media.
  • An embodiment of the present disclosure also proposes an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to invoke the instructions stored in the memory to execute the above method.
  • An embodiment of the present disclosure also provides a computer program product, including computer-readable codes, or a non-volatile computer-readable storage medium carrying computer-readable codes, when the computer-readable codes are stored in a processor of an electronic device When running in the electronic device, the processor in the electronic device executes the above method.
  • Electronic devices may be provided as terminals, servers, or other forms of devices.
  • FIG. 3 shows a block diagram of an electronic device 800 according to an embodiment of the present disclosure.
  • the electronic device 800 may be a terminal such as a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, or a personal digital assistant.
  • electronic device 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814 , and the communication component 816.
  • the processing component 802 generally controls the overall operations of the electronic device 800, such as those associated with display, telephone calls, data communications, camera operations, and recording operations.
  • the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method. Additionally, processing component 802 may include one or more modules that facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802 .
  • the memory 804 is configured to store various types of data to support operations at the electronic device 800 . Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and the like.
  • the memory 804 can be implemented by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic or Optical Disk Magnetic Disk
  • the power supply component 806 provides power to various components of the electronic device 800 .
  • Power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for electronic device 800 .
  • the multimedia component 808 includes a screen providing an output interface between the electronic device 800 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user.
  • the touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may not only sense a boundary of a touch or swipe action, but also detect duration and pressure associated with the touch or swipe action.
  • the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capability.
  • the audio component 810 is configured to output and/or input audio signals.
  • the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 800 is in operation modes, such as call mode, recording mode and voice recognition mode. Received audio signals may be further stored in memory 804 or sent via communication component 816 .
  • the audio component 810 also includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which may be a keyboard, a click wheel, a button, and the like. These buttons may include, but are not limited to: a home button, volume buttons, start button, and lock button.
  • Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of electronic device 800 .
  • the sensor component 814 can detect the open/closed state of the electronic device 800, the relative positioning of components, such as the display and the keypad of the electronic device 800, the sensor component 814 can also detect the electronic device 800 or a Changes in position of components, presence or absence of user contact with electronic device 800 , electronic device 800 orientation or acceleration/deceleration and temperature changes in electronic device 800 .
  • Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact.
  • Sensor assembly 814 may also include an optical sensor, such as a complementary metal-oxide-semiconductor (CMOS) or charge-coupled device (CCD) image sensor, for use in imaging applications.
  • CMOS complementary metal-oxide-semiconductor
  • CCD charge-coupled device
  • the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor or a temperature sensor.
  • the communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices.
  • the electronic device 800 can access a wireless network based on a communication standard, such as a wireless network (WiFi), a second generation mobile communication technology (2G) or a third generation mobile communication technology (3G), or a combination thereof.
  • the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra Wide Band (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID Radio Frequency Identification
  • IrDA Infrared Data Association
  • UWB Ultra Wide Band
  • Bluetooth Bluetooth
  • electronic device 800 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A programmable gate array (FPGA), controller, microcontroller, microprocessor or other electronic component implementation for performing the methods described above.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGA field programmable A programmable gate array
  • controller microcontroller, microprocessor or other electronic component implementation for performing the methods described above.
  • a non-volatile computer-readable storage medium such as the memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to implement the above method.
  • FIG. 4 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure.
  • electronic device 1900 may be provided as a server.
  • electronic device 1900 includes processing component 1922 , which further includes one or more processors, and a memory resource represented by memory 1932 for storing instructions executable by processing component 1922 , such as application programs.
  • the application programs stored in memory 1932 may include one or more modules each corresponding to a set of instructions.
  • the processing component 1922 is configured to execute instructions to perform the above method.
  • Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input-output (I/O) interface 1958 .
  • the electronic device 1900 can operate based on the operating system stored in the memory 1932, such as the Microsoft server operating system (Windows Server TM ), the graphical user interface-based operating system (Mac OS X TM ) introduced by Apple Inc., and the multi-user and multi-process computer operating system (Unix TM ), a free and open-source Unix-like operating system (Linux TM ), an open-source Unix-like operating system (FreeBSD TM ), or the like.
  • Microsoft server operating system Windows Server TM
  • Mac OS X TM graphical user interface-based operating system
  • Unix TM multi-user and multi-process computer operating system
  • Linux TM free and open-source Unix-like operating system
  • FreeBSD TM open-source Unix-like operating system
  • a non-transitory computer-readable storage medium such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to implement the above-mentioned method.
  • the present disclosure can be a system, method and/or computer program product.
  • a computer program product may include a computer readable storage medium having computer readable program instructions thereon for causing a processor to implement various aspects of the present disclosure.
  • a computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device.
  • a computer readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Computer-readable storage media include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory), static random access memory (SRAM), compact disc read only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded device, such as a printer with instructions stored thereon A hole card or a raised structure in a groove, and any suitable combination of the above.
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory static random access memory
  • SRAM static random access memory
  • CD-ROM compact disc read only memory
  • DVD digital versatile disc
  • memory stick floppy disk
  • mechanically encoded device such as a printer with instructions stored thereon
  • a hole card or a raised structure in a groove and any suitable combination of the above.
  • computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., pulses of light through fiber optic cables), or transmitted electrical signals.
  • Computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or downloaded to an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • a network adapter card or a network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .
  • Computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or Source or object code written in any combination, including object-oriented programming languages—such as Smalltalk, C++, etc., and conventional procedural programming languages—such as the “C” language or similar programming languages.
  • Computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as via the Internet using an Internet service provider). connect).
  • LAN local area network
  • WAN wide area network
  • an electronic circuit such as a programmable logic circuit, field programmable gate array (FPGA), or programmable logic array (PLA)
  • FPGA field programmable gate array
  • PDA programmable logic array
  • These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that when executed by the processor of the computer or other programmable data processing apparatus , producing an apparatus for realizing the functions/actions specified in one or more blocks in the flowchart and/or block diagram.
  • These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause computers, programmable data processing devices and/or other devices to work in a specific way, so that the computer-readable medium storing instructions includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks in flowcharts and/or block diagrams.
  • each block in a flowchart or block diagram may represent a module, a portion of a program segment, or an instruction that includes one or more Executable instructions.
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Abstract

提供了一种用于车辆的向紧急呼叫中心发送信息的方法及装置、电子设备和存储介质，方法包括：响应于紧急呼叫被触发，获取舱内乘员的影像信息（11）；基于所述影像信息，检测舱内乘员的流血情况（12）；响应于检测到流血情况，将所述流血情况发送给紧急呼叫中心（13）。

Description

用于车辆的向紧急呼叫中心发送信息的方法及装置
本申请要求2021年08月31日提交、申请号为202111016361.1，发明名称为“用于车辆的向紧急呼叫中心发送信息的方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本公开涉及计算机技术领域，尤其涉及一种用于车辆的向紧急呼叫中心发送信息的方法及装置、电子设备、计算机程序产品和存储介质。
背景技术
在道路运输过程中，汽车可能会发生交通事故，此时如果救援人员可以及时获知事故信息并开展救援，那么事故当事人便能够得到及时的救援，从而减少财产损失，降低人员伤亡。
为了能够让救援人员及时获知事故信息，可以在汽车上集成车载紧急呼叫(emergency call，eCall)系统，eCall系统属于车联网典型应用。基于汽车传感、移动通信、卫星定位等技术，在事故发生后第一时间与公共救援中心取得联系，并自动将车辆位置及车辆信息发送至救援中心，救援中心确认事故后对事故人员进行救援。
发明内容
本公开提出了一种信息发送技术方案。
根据本公开的一方面，提供了一种用于车辆的向紧急呼叫中心发送信息的方法，包括：
响应于紧急呼叫被触发，获取舱内乘员的影像信息；
基于所述影像信息，检测舱内乘员的流血情况；
响应于检测到流血情况，将所述流血情况发送给紧急呼叫中心。
在一种可能的实现方式中，所述基于所述影像信息，检测舱内乘员的流血情况，包括：
对所述影像信息进行人脸检测和/或人体检测，确定出所述舱内的乘员；
在所述乘员的面部和/或体表进行血液检测，确定舱内乘员的流血情况。
在一种可能的实现方式中，所述基于所述影像信息，检测舱内乘员的流血情况，包括：
基于血液的颜色信息和血流的形状信息，基于所述影像信息检测所述乘员是否流血。
在一种可能的实现方式中，所述基于所述影像信息，检测舱内乘员的流血情况，包括：
基于所述影像信息，检测所述舱内的乘员的体表区域；
将所述乘员的体表区域划分为多个检测区域；
在每个所述检测区域中检测血液信息，得到各所述检测区域的区域检测结果；
基于各所述检测区域的区域检测结果，确定所述乘员的流血情况。
在一种可能的实现方式中，所述基于所述影像信息，检测所述舱内的乘员的体表区域，包括：
基于所述影像信息，检测舱内的乘员的人脸表面区域；
所述将所述乘员的体表区域划分为多个检测区域，包括：
将所述乘员的人脸表面区域划分为多个检测区域。
在一种可能的实现方式中，所述在每个所述检测区域中检测血液信息，得到各所述检测区域的区域检测结果，包括：
基于每个检测区域中血流的形状和面积，确定每个所述检测区域存在流血情况的第一置信度；
确定各相邻的检测区域之间是否存在相接的血流；
响应于确定所述检测区域中的第一检测区域与相邻的第二检测区域存在相接的血流，将所述多个第一检测区域和所述第二检测区域的置信度升高为第二置信度；
所述基于各所述检测区域的区域检测结果，确定所述乘员的流血情况，包括：
在所述第一置信度或所述第二置信度超过置信度阈值的情况下，确定所述乘员流血；
基于所述每个检测区域中的血流的面积，确定流血的严重程度，所述流血的严重程度与所述各所述检测区域的血流的面积和正相关。
在一种可能的实现方式中，所述基于所述影像信息，检测舱内乘员的流血情况，包括：
响应于基于所述影像信息检测到所述舱内的乘员流血，确定流血的身体部位以及血流的方向；
基于所述流血的身体部位以及血流的方向，将血流的起始端所在的身体部位，作为出血部位。
在一种可能的实现方式中，所述方法还包括：
根据所述影像信息，确定所述舱内乘员的身体姿态；
在所述身体姿态为预设的异常身体姿态、且所述异常身体姿态的持续时长超过设定时长的情况下，确定所述舱内乘员的身体姿态为异常身体姿态。
在一种可能的实现方式中，所述方法还包括：
在确定所述舱内乘员的身体姿态为预设的骨折姿态的情况下，确定所述舱内乘员存在骨折状况。
在一种可能的实现方式中，所述方法还包括：
基于所述影像信息确定所述乘员的生命体征指标，所述生命体征指标包括以下至少一项：
呼吸频率、血压、心率；
将所述生命体征指标发送给紧急呼叫中心。
在一种可能的实现方式中，所述方法还包括：
基于确定的所述乘员的流血情况、异常身体姿态、生命体征指标中的至少一项，确定舱内乘员的受伤严重级别；
将所述受伤严重级别发送给紧急呼叫中心。
根据本公开的一方面，提供了一种用于车辆的向紧急呼叫中心发送信息的装置，包括：
影像信息获取单元，用于响应于紧急呼叫被触发，获取舱内乘员的影像信息；
流血情况检测单元，用于基于所述影像信息，检测舱内乘员的流血情况；
流血情况发送单元，用于响应于检测到流血情况，将所述流血情况发送给紧急呼叫中心。
在一种可能的实现方式中，所述流血情况检测单元，包括：
乘员检测子单元，用于对所述影像信息进行人脸检测和/或人体检测，确定出所述舱内的乘员；
第一流血情况确定子单元，用于在所述乘员的面部和/或体表进行血液检测，确定舱内乘员的流血情况。
在一种可能的实现方式中，所述流血情况检测单元，用于基于血液的颜色信息和血流的形状信息，基于所述影像信息检测所述乘员是否流血。
在一种可能的实现方式中，所述流血情况检测单元，包括：
体表区域检测子单元，用于基于所述影像信息，检测所述舱内的乘员的体表区域；
检测区域划分子单元，用于将所述乘员的体表区域划分为多个检测区域；
区域检测结果确定子单元，用于在每个所述检测区域中检测血液信息，得到各所述检测区域的区域检测结果；
第二流血情况确定子单元，用于基于各所述检测区域的区域检测结果，确定所述乘员的流血情况。
在一种可能的实现方式中，所述体表区域检测子单元，用于基于所述影像信息，检测舱内的乘员的人脸表面区域；
所述检测区域划分子单元，用于将所述乘员的人脸表面区域划分为多个检测区域。
在一种可能的实现方式中，所述区域检测结果确定子单元，用于基于每个检测区域中血流的形状和面积，确定每个所述检测区域存在流血情况的第一置信度；确定各相邻的检测区域之间是否存在相接的血流；响应于确定所述检测区域中的第一检测区域与相邻的第二检测区域存在相接的血流，将所述多个第一检测区域和所述第二检测区域的置信度升高为第二置信度；
所述第二流血情况确定子单元，用于在所述第一置信度或所述第二置信度超过置信度阈值的情况下，确定所述乘员流血；基于所述每个检测区域中的血流的面积，确定流血的严重程度，所述流血的严重程度与所述各所述检测区域的血流的面积和正相关。
在一种可能的实现方式中，所述流血情况检测单元，包括：
流血部位检测子单元，用于响应于基于所述影像信息检测到所述舱内的乘员流血， 确定流血的身体部位以及血流的方向；
出血部位检测子单元，用于基于所述流血的身体部位以及血流的方向，将血流的起始端所在的身体部位，作为出血部位。
在一种可能的实现方式中，所述装置还包括：
身体姿态确定单元，用于根据所述影像信息，确定所述舱内乘员的身体姿态；
异常身体姿态确定单元，用于在所述身体姿态为预设的异常身体姿态、且所述异常身体姿态的持续时长超过设定时长的情况下，确定所述舱内乘员的身体姿态为异常身体姿态。
在一种可能的实现方式中，所述装置还包括：
骨折状况检测单元，用于在确定所述舱内乘员的身体姿态为预设的骨折姿态的情况下，确定所述舱内乘员存在骨折状况。
在一种可能的实现方式中，所述装置还包括：
生命体征指标确定单元，用于基于所述影像信息确定所述乘员的生命体征指标，所述生命体征指标包括以下至少一项：
呼吸频率、血压、心率；
生命体征指标发送单元，用于将所述生命体征指标发送给紧急呼叫中心。
在一种可能的实现方式中，所述装置还包括：
受伤严重级别确定单元，用于基于确定的所述乘员的流血情况、异常身体姿态、生命体征指标中的至少一项，确定舱内乘员的受伤严重级别；
受伤严重级别发送单元，用于将所述受伤严重级别发送给紧急呼叫中心。
根据本公开的一方面，提供了一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为调用所述存储器存储的指令，以执行上述方法。
根据本公开的一方面，提供了一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述方法。
根据本公开的一方面，提供了一种计算机程序产品，包括计算机可读代码，或者承载有计算机可读代码的非易失性计算机可读存储介质，当所述计算机可读代码在电子设备的处理器中运行时，所述电子设备中的处理器执行用于实现上述方法。
在本公开实施例中，通过响应于紧急呼叫被触发，获取舱内乘员的影像信息，并基于所述影像信息，检测舱内乘员的流血情况，然后响应于检测到流血情况，将所述流血情况发送给紧急呼叫中心。由此，能够确定事故中乘员的受伤情况，以便紧急呼叫中心针对事故中乘员的受伤情况，合理调度救援力量，便针对流血情况严重的场景，缩短或者省略呼叫中心人员的询问过程，争分夺秒的进行抢救。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，而非限制本公开。根据下面参考附图对示例性实施例的详细说明，本公开的其它特征及方面将变得清楚。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，这些附图示出了符合本公开的实施例，并与说明书一起用于说明本公开的技术方案。
图1示出根据本公开实施例的信息发送方法的流程图；
图2示出根据本公开实施例的信息发送装置的框图；
图3示出根据本公开实施例的一种电子设备的框图；
图4示出根据本公开实施例的一种电子设备的框图。
具体实施方式
以下将参考附图详细说明本公开的各种示例性实施例、特征和方面。附图中相同的附图标记表示功能相同或相似的元件。尽管在附图中示出了实施例的各种方面，但是除非特别指出，不必按比例绘制附图。
在这里专用的词“示例性”意为“用作例子、实施例或说明性”。这里作为“示例性”所说明的任何实施例不必解释为优于或好于其它实施例。
本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中术语“至少一种”表示多种中的任意一种或多种中的至少两种的任意组合，例如，包括A、B、C中的至少一种，可以表示包括从A、B和C构成的集合中选择的任意一个或多个元素。
另外，为了更好地说明本公开，在下文的具体实施方式中给出了众多的具体细节。本领域技术人员应当理解，没有某些具体细节，本公开同样可以实施。在一些实例中，对于本领域技术人员熟知的方法、手段、元件和电路未作详细描述，以便于凸显本公开的主旨。
如背景技术描述，紧急呼叫服务可以缩短救援时间，降低车辆事故中被救援人员的死亡率。然而在相关技术中，在事故发生后难以判断事故损伤程度，更无法确认车内人员的伤亡情况，在救援中心存在较多紧急呼叫的情况下，救援中心也就无法合理地调度救援力量。
在本公开实施例中，通过响应于紧急呼叫被触发，获取舱内乘员的影像信息，并基于所述影像信息，检测舱内乘员的流血情况，然后响应于检测到流血情况，将所述流血情况发送给紧急呼叫中心。由此，能够确定事故中乘员的受伤情况，以便紧急呼叫中心针对事故中乘员的受伤情况，合理调度救援力量，便针对流血情况严重的场景，缩短或者省略呼叫中心人员的询问过程，争分夺秒地进行抢救。
在一种可能的实现方式中，该方法的执行主体可以是安装于车辆上的智能驾驶控制装置。在一种可能的实现方式中，该方法可以由终端设备或服务器或其它处理设备执行。其中，终端设备可以是车载设备、用户设备(User Equipment，UE)、移动设备、用户终 端、终端、蜂窝电话、无绳电话、个人数字助理(Personal Digital Assistant，PDA)、手持设备、计算设备或者可穿戴设备等。其中，车载设备可以是车舱内的车机或者域控制器，还可以是ADAS(Advanced Driving Assistance System，高级驾驶辅助系统)、OMS(Occupant Monitoring System，乘员监控系统)或者DMS(Driver Monitoring System，驾驶员监控系统)中用于执行信息发送方法的设备主机等。在一些可能的实现方式中，所述信息发送方法可以通过处理器调用存储器中存储的计算机可读指令的方式来实现。该发送方法步骤的执行主体可以为硬件执行，或者通过处理器运行计算机可执行代码的方式执行。
为便于描述，本说明书一个或多个实施例中，用于车辆的向紧急呼叫中心发送信息的方法的执行主体可以是车辆中的车载设备，后文以执行主体为车载设备为例，对该方法的实施方式进行介绍。可以理解，该方法的执行主体为车载设备只是一种示例性的说明，并不应理解为对该方法的限定。图1示出根据本公开实施例的用于车辆的向紧急呼叫中心发送信息的方法的流程图，如图1所示，所述用于车辆的向紧急呼叫中心发送信息的方法包括：
在步骤S11中，响应于紧急呼叫被触发，获取舱内乘员的影像信息；
这里的影像信息为车辆舱内乘员的影像信息，这里的车辆可以是私家车、共享汽车、网约车、出租车、货车等类型的车辆中的至少一种车辆，本公开对车辆的具体类型不作限定。
这里的影像信息可以是舱内的乘员所在区域的影像信息，该影像信息可以通过设置于车辆的舱内或舱外的车载影像采集设备来采集，车载影像采集设备可以是车载摄像头或配置有摄像头的影像采集装置。该摄像头既可以是用于采集车辆内部影像信息的摄像头，也可以是用于采集车辆外部影像信息的摄像头。
例如，该摄像头可以包括DMS中的摄像头和/或OMS中的摄像头等，这些摄像头可以用于采集车辆内部的影像信息；该摄像头还可以包括ADAS中的摄像头，可以用于采集车辆外部的影像信息。当然，车载影像采集设备也可以是其它系统中的摄像头，或者也可以是单独配置的摄像头，本公开实施例对具体的车载影像采集设备不做限定。
这里的影像信息的载体可以是二维图像或视频，例如，影像信息具体可以是可见光图像/视频，或者红外光图像/视频；也可以是雷达扫描的点云构成的三维影像，等等，具体可依据实际应用场景而定，本公开对此不做限定。
可以通过与车载影像采集设备之间建立的通信连接获取其采集的影像信息。在一个示例中，车载影像采集设备可以实时地将采集的影像信息通过总线或无线通信通道传输至车载控制器或远程服务器，车载控制器或远程服务器可以通过总线或无线通信通道接收实时的影像信息。
在步骤S12中，基于所述影像信息，检测舱内乘员的流血情况；
可以基于图像处理技术检测乘员身上是否有血液，以确定乘员是否存在流血情况。在一个示例中，可以通过神经网络来检测乘员的流血情况，或者，也可以通过阈值分割 等目标检测技术来检测图像中的血液，以检测舱内乘员的流血情况，对于检测乘员流血情况的具体实现方式，可参见本公开提供的可能的实现方式，此处不做赘述。
在步骤S13中，响应于检测到流血情况，将所述流血情况发送给紧急呼叫中心。
向紧急呼叫中心发送的血流情况的具体实现方式可以有多种，例如，可以是舱内乘员存在血流情况还是未存在血流情况；或者，在存在流血情况时，也可以发送更为具体的流血情况，例如，具体发生流血的部位、流血的严重程度等等。
由此，通过将流血情况发送给紧急呼叫中心，紧急呼叫中心便能够确定紧急呼叫发起方是否存在流血的情况，并在确定存在流血情况时，提供针对性的救援措施，例如携带对应的止血用品、输血用品，增派处理流血情况的医生等等。
在本公开实施例中，通过响应于紧急呼叫被触发，获取舱内乘员的影像信息，并基于所述影像信息，检测舱内乘员的流血情况，然后响应于检测到流血情况，将所述流血情况发送给紧急呼叫中心。由此，能够确定事故中乘员的受伤情况，以便紧急呼叫中心针对事故中乘员的受伤情况，合理调度救援力量，便针对流血情况严重的场景，缩短或者省略呼叫中心人员的询问过程，争分夺秒的进行抢救。
在一种可能的实现方式中，所述基于所述影像信息，检测舱内乘员的流血情况，包括：对所述影像信息进行人脸检测和/或人体检测，确定出所述舱内的乘员；在所述乘员的面部和/或体表进行血液检测，确定舱内乘员的流血情况。
在得到舱内的影像信息后，可以基于影像信息，对舱内进行人体检测和/或人脸检测，得到所述舱内的人体检测结果和/或人脸检测结果，并可以基于所述舱内的人体检测结果和/或人脸检测结果得到所述舱内的乘员检测结果。例如，可以将所述舱内的人体检测结果和/或人脸检测结果作为所述舱内的乘员检测结果。又如，可以对所述舱内的人体检测结果和/或人脸检测结果进行处理后得到所述舱内的乘员检测结果。
在本公开实施例中，对所述舱内进行人体检测和/或人脸检测，得到所述舱内的人体检测结果和/或人脸检测结果，该检测结果包括人体和/或人脸的位置信息。例如，在检测到一个乘员的情况下，所述乘员检测结果包括该乘员的位置信息；在检测到多个乘员的情况下，所述乘员检测结果可以包括检测到的各个乘员的位置信息。
所述乘员的位置信息可以采用所述乘员的边界框的位置信息来表示。然后，在该边界框所框选的图像中，对乘员进行血液检测。或者，乘员的位置信息可以采用所述乘员的边界轮廓的位置信息来表示，然后，在该边界轮廓所包围的图像中，对乘员进行血液检测。
基于人脸检测能够得到乘员人脸的位置，在检测到人脸的情况下，可以对乘员的面部进行血液检测，来确定舱内乘员的流血情况；基于人体检测能够得到乘员人体的位置，在检测到人体的情况下，可以对乘员的体表进行血液检测，来确定舱内乘员的流血情况。
在本公开实施例中，通过对所述影像信息进行人脸检测和/或人体检测，确定出所述舱内的乘员，然后在所述乘员的面部和/或体表进行血液检测，确定舱内乘员的流血情况。由此，先对影像信息进行人脸检测和/或人体检测，在所述乘员的面部和/或体表进行血液 检测，不仅能够缩小后续进行血液检测的图像范围，提高检测效率，而且能够减少乘员身体以为的其它区域对血液检测的干扰，提高流血情况检测的准确性。
具体进行血液检测的实现方式可以是基于血液的颜色信息和血流的形状信息来检测，那么，在一种可能的实现方式中，所述基于所述影像信息，检测舱内乘员的流血情况，包括：基于血液的颜色信息和血流的形状信息，基于所述影像信息检测所述乘员是否流血。
乘员在发生流血后，血液的颜色往往是鲜红色的，在计算机中，对颜色的表示是通过定义颜色空间中的颜色参数来实现的，常用的颜色空间是红绿蓝(RGB)颜色空间，另外，基于国际照明委员会(Commission Internationale de L'Eclairage，CIE)标准色度学系统制定的标准，相关技术中还存在HSL、LMS、CMYK、CIE YUV、HSB(HSV)、YCbCr等颜色空间。颜色空间的表达形式是多样的，不同的颜色空间可具有不同的特性，不同颜色空间的颜色参数之间可以互相转换。
依据不同的颜色空间对颜色参数的定义，可以从所述影像信息中解析出相应的颜色参数，图像在计算机中存储时，会存储默认颜色空间下，计算机中的大部分图像的默认颜色空间为RGB颜色空间，RGB颜色空间分为红(R)、绿(G)、蓝(B)三个颜色分量，每一颜色分量的取值范围为0～255。计算机在从存储介质中对所述影像信息进行读取时，通过数字图像处理技术对影像信息进行读取，即能够得到影像信息中的每个像素点在默认颜色空间中的三维分量，即得到了影像信息在默认颜色空间中的颜色参数。
每个像素点都会具备一个颜色参数，不同的颜色参数代表不同的颜色，因此，基于该颜色参数即可确定该像素点的颜色。而针对血液的颜色，其颜色参数可以是一个范围，在一个示例中，以RGB颜色空间为例，血液的颜色参数的范围可以表示为(150,0,0)-(250,50,50)，当像素点的颜色范围在该范围内时，可以认为是血液的颜色，进一步地，再根据血液的形状进行核验。
此外，对于流血情况的检测还可以基于神经网络技术实现，在通过神经网络技术进行图像处理的过程中，往往会通过图像卷积操作对图像特征进行提取，该操作中会对图像的像素值进行提取，那么，血液的颜色信息会被提取为高维特征表示，神经网络可以依据该高维特征来确定属于血液颜色的高维特征，进而检测流血情况。
由于乘员的服饰、配饰等附着物的颜色可能与血液的颜色相近似，因此，还可以进一步再依据血流的形状来确定乘员是否流血，血流的形状可以依据图像中真实的血流的形状得到。
血液检测可以依据深度神经网络来实现，用于进行血液检测的神经网络例如可以是基于注意力机制的Seq2Seq模型、Tensorflow模型等。该神经网络可以是已训练好的网络，也可根据乘员流血的影像信息的特性，采用图像内容中包含乘员流血情况的图像数据集对神经网络进行训练，通过标注图像数据集中的流血区域，对神经网络进行训练，使神经网络在检测血液时准确率较高，能够准确地检测到乘员面部和/或体表的血液。
在本公开实施例中，通过基于血液的颜色信息和血流的形状信息，基于所述影像信 息检测所述乘员是否流血，能够准确地检测到舱内乘员的流血情况。
在一种可能的实现方式中，所述基于所述影像信息，检测舱内乘员的流血情况，包括：基于所述影像信息，检测所述舱内的乘员的体表区域；将所述乘员的体表区域划分为多个检测区域；在每个所述检测区域中检测血液信息，得到各所述检测区域的区域检测结果；基于各所述检测区域的区域检测结果，确定所述乘员的流血情况。
基于影像信息，能够检测到舱内乘员的体表区域，该体表区域可基于图像中的坐标来表示，针对该体表区域，可以划分为多个检测区域。具体划分检测区域的方式可以有多种，例如，可以用网格对体表区域划分，该划分方式可以将体表露出的部分(例如人脸、脖子等)划分为面积相同的网格；又如，还可以根据体表中的人体部位对体表区域进行划分，可以将体表的胳膊划分为一个检测区域，将胸部划分为一个检测区域，将腹部划分为一个检测区域，等等，在依据人体部位对体表区域进行划分时，可以对人体进行人体关键点检测，得到用来标识人体部位的关键点，然后依据该关键点来对体表区域进行划分，得到多个检测区域。此外，人体表区域的划分方式还可以有多种，本公开对此不作限定。
在得到多个检测区域后，可以在每个检测区域中检测血液信息，具体检测血液信息的方式可以参考本公开提供的可能的实现方式，例如可以是依据血液颜色和血流形状来检测血液，或者可以是通过训练的神经网络来检测血液，此处不做赘述。
在每个检测区域中检测血液信息后，即可得到各检测区域的区域检测结果，该区域检测结果可以是存在流血情况，或不存在流血情况，在存在流血情况时，还可以具体到存在流血的面积，检测到血流在检测区域中的具体位置信息。
然后基于各检测区域的区域检测结果，可以对各检测区域的区域检测结果进行加权融合，确定乘员的流血情况；或者，可以综合各区域检测结果，确定乘员存在流血的置信度和流血严重程度，具体可参见本公开提供的可能的实现方式，此处不做赘述。
在本公开实施例中，通过基于影像信息，检测舱内的乘员的体表区域；将乘员的体表区域划分为多个检测区域；在每个检测区域中检测血液信息，得到各检测区域的区域检测结果，由此，依据各检测区域的区域检测结果，确定乘员的流血情况，能够提高确定的流血情况的准确度。
在一种可能的实现方式中，所述基于所述影像信息，检测所述舱内的乘员的体表区域，包括：基于所述影像信息，检测舱内的乘员的人脸表面区域；所述将所述乘员的体表区域划分为多个检测区域，包括：将所述乘员的人脸表面区域划分为多个检测区域。
基于所述影像信息，能够检测舱内的乘员的人脸表面区域，该人脸表面区域可基于图像中的坐标来表示，针对该人脸表面区域，可以划分为多个检测区域。具体划分人脸表面区域的方式可以有多种，例如，可以用网格对人脸表面区域划分，该网格可以是面积相同的网格，这样，可以将人脸表面区域通过面积相同的网格进行划分；又如，还可以根据人脸表面中的部位对人脸表面区域进行划分，可以将额头划分为一个检测区域，将鼻子两侧分别划分为一个检测区域，将嘴部及其以下部分划分为一个检测区域，等等， 在依据人脸部位对人脸表面区域进行划分时，可以对人脸表面进行人脸关键点检测，得到用来标识人脸部位的关键点，然后依据该关键点来对人脸表面区域进行划分，得到多个检测区域。此外，人脸表面区域的划分方式还可以有多种，本公开对此不作限定。
在本公开实施例中，通过基于影像信息，检测舱内的乘员的人脸表面区域，然后将乘员的人脸表面区域划分为多个检测区域，由此，依据各检测区域的区域检测结果，确定乘员面部的流血情况，能够提高确定的流血情况的准确度。
在一种可能的实现方式中，所述在每个所述检测区域中检测血液信息，得到各所述检测区域的区域检测结果，包括：基于每个检测区域中血流的形状和面积，确定每个所述检测区域存在流血情况的第一置信度；确定各相邻的检测区域之间是否存在相接的血流；响应于确定所述检测区域中的第一检测区域与相邻的第二检测区域存在相接的血流，将所述多个第一检测区域和所述第二检测区域的置信度升高为第二置信度；所述基于各所述检测区域的区域检测结果，确定所述乘员的流血情况，包括：在所述第一置信度或所述第二置信度超过置信度阈值的情况下，确定所述乘员流血；基于所述每个检测区域中的血流的面积，确定流血的严重程度，所述流血的严重程度与所述各所述检测区域的血流的面积和正相关。
在基于图像处理技术在影像信息中检测血流时，可以看作确定图像中的像素点是否属于血流的二分类问题，可基于深度学习中的图像分割技术来实现，这样能够得到检测区域中属于血流的像素点所在的位置，多个分类为血流的像素点相连接即会构成血流区域，即构成一个血流形状。而血流的面积，可以是多个分类为血流的像素点在图像中的面积，或者，也可以换算为在乘员体表的真实面积。
基于每个检测区域中血流的形状和面积，能够确定每个检测区域存在流血情况的第一置信度，具体可以基于神经网络来确定，第一置信度表征单个检测区域中存在血流情况的可信程度。第一置信度可以与该区域的血流的面积正相关，即面积越大第一置信度越高；检测区域中血流的形状与真实血流的形状越接近，则第一置信度越高。
在得到每个检测区域的第一置信度后，可进一步确定各检测区域之间是否存在相接的血流，具体可以依据各检测区域中确定的血液的位置来确定，各检测区域中血液的位置可以是在影像信息中的位置，如果血液的位置在影像信息中相邻接，即可确定检测区域之间存在相接的血流。
由于检测区域之间存在相接的血流，表明乘员的流血面积要大于单个检测区域中的血流的面积，那么乘员存在流血的可信程度应当进一步提高，因此，响应于确定检测区域中的第一检测区域与相邻的第二检测区域存在相接的血流，将多个第一检测区域和所述第二检测区域的置信度升高为第二置信度，以提高第一检测区域和第二检测区域中存在流血情况的可信程度。
例如，第一检测区域和第二检测区域的第一置信度分别为0.6和0.7，在确定第一检测区域和第二检测区域存在相接的血流的情况下，可以将第一检测区域和第二检测区域的置信度分别升高为0.7和0.8，得到第二置信度。需要说明的是，具体对第一置信度升高的 幅度可以依据实际需求确定，本公开对此不作具体限定。
由于置信度能够表征乘员存在流血情况的可信程度，因此，可以预先设置一个置信度阈值，在第一置信度或第二置信度超过置信度阈值的情况下，确定乘员流血。该置信度阈值的具体设置可根据实际需求确定，本公开对此不作具体限定。
此外，由于血流的面积越大，则表明流血的程度越严重，因此，在一种可能的实现方式中，可以基于每个检测区域中的血流的面积，确定流血的严重程度，流血的严重程度与各所述检测区域的血流的面积和正相关。
在本公开实施例中，基于每个检测区域中血流的形状和面积，确定每个所述检测区域存在流血情况的第一置信度；确定各相邻的检测区域之间是否存在相接的血流；响应于确定所述检测区域中的第一检测区域与相邻的第二检测区域存在相接的血流，将所述多个第一检测区域和所述第二检测区域的置信度升高为第二置信度；在所述第一置信度或所述第二置信度超过置信度阈值的情况下，确定所述乘员流血。由此，能够提高确定的流血情况的准确度。此外，基于所述每个检测区域中的血流的面积，确定流血的严重程度，将流血的严重程度发送给紧急呼叫中心，以便紧急呼叫中心能够及时地获知呼叫方流血严重程度，合理地调度救援力量，提供针对性的救援。
在一种可能的实现方式中，所述基于所述影像信息，检测舱内乘员的流血情况，包括：响应于基于所述影像信息检测到所述舱内的乘员流血，确定流血的身体部位以及血流的方向；基于所述流血的身体部位以及血流的方向，将血流的起始端所在的身体部位，作为出血部位。
基于人体关键点检测，能够确定乘员体表的身体部位，由此，能够确定出血液所在的身体部位，而由于血液存在流动性，因此血液可能是在某个部位出血后跨越了多个身体部位，由此，可以进一步确定血流的方向，基于血流的方向，将血流的起始端所在的身体部位，作为出血部位。
在确定血流方向的过程中，可以基于影像信息中的多个视频帧来确定，在多个视频帧中进行血液检测，依据多个视频帧中的血流能够确定血流逐渐增多的方向，即可将该方向作为血流的方向。
在本公开实施例中，响应于基于所述影像信息检测到所述舱内的乘员流血，确定流血的身体部位以及血流的方向；基于所述流血的身体部位以及血流的方向，将血流的起始端所在的身体部位，作为出血部位。由此，能够准确地确定出血部位，将出血部位作为流血情况发送给紧急呼叫中心，便于紧急呼叫中心明确呼叫方的出血部位，依据出血部位能够明确出血的严重程度，合理地调度救援力量，以便提供针对性的救援。
在一种可能的实现方式中，所述方法还包括：根据所述影像信息，确定所述舱内乘员的身体姿态；在所述身体姿态为预设的异常身体姿态、且所述异常身体姿态的持续时长超过设定时长的情况下，确定所述舱内乘员的身体姿态为异常身体姿态。
在本公开实施例中，能够基于影像信息，能够确定舱内乘员的身体姿态，示例性地，可以通过图像识别技术来确定乘员的身体姿态。身体姿态检测可以基于人体关键检测来 确定，作为该实现方式的一个示例，可以预先设置要检测的多个人体关键点，例如，可以设置人体骨架中包含17个关键点，分别指示人体的各部位，通过检测这17个关键点，根据这17个关键点之间的位置关系，即可得到人体各部位之间的位置关系，人体各部位之间的位置关系即为身体姿态的具体表现形式。
作为该实现方式的一个示例，可以将影像信息输入骨干网络，经由骨干网络对影像信息进行特征提取，得到特征图，然后基于特征图来检测人体的关键点的位置，进而得到人体姿态。其中，骨干网络可以采用ResNet、MobileNet等网络结构，在此不做限定。
在确定舱内乘员的身体姿态后，可以判断该身体姿态是否为预设的异常身体姿态，在确定身体姿态为预设的异常身体姿态的情况下，确定舱内乘员的健康状况为异常状况。预设的异常身体姿态包括以下至少一项：身体朝向一侧倾斜、头部向下倾斜、脸朝上。由于乘员的身体姿态能够在一定程度上体现用户的身体的健康状况，当事故发生后，乘员在受伤的情况下，往往身体无法保持挺直的姿态，则会呈现身体朝向一侧倾斜、头部向下倾斜或者仰面朝上等异常的姿态。这些身体姿态能够准确地表示用户当前的健康状态为异常状况。
因此，可以预先设置该些异常身体姿态，当确定舱内乘员的身体姿态后，即可判断舱内乘员的身体状态是否为预设的异常身体姿态，在确定所述身体姿态为预设的异常身体姿态的情况下，确定所述舱体内乘员的健康状况为异常状况。
此外，为提高异常状况检测的准确率，可以在身体姿态为预设的异常身体姿态、且所述异常身体姿态的持续时长超过设定时长的情况下，确定所述舱内乘员的身体姿态为异常身体姿态。
在本公开实施例中，根据所述影像信息，确定所述舱内乘员的身体姿态；在所述身体姿态为预设的异常身体姿态、且所述异常身体姿态的持续时长超过设定时长的情况下，确定所述舱内乘员的身体姿态为异常身体姿态。由此，能够准确地确定出乘员的异常情况，以便后续准确地确定乘员受伤严重级别，将乘员受伤严重级别发送给紧急呼叫中心，以便紧急呼叫中心优先抢救受伤严重的乘客。
在一种可能的实现方式中，所述方法还包括：在确定所述舱内乘员的身体姿态为预设的骨折姿态的情况下，确定所述舱内乘员存在骨折状况。
乘员在发生骨折的情况下，其身体姿态是明显异于正常的身体姿态的，例如，乘员的非关节处发生了弯折，这可以明确乘员存在骨折情况。例如，在手臂的非关节处存在弯折，则可以确定乘员手臂存在骨折的情况，又如，在腿部的非关节处存在弯折，也可以确定乘员腿部存在骨折的情况。
在一种可能的实现方式中，所述方法还包括：基于所述影像信息确定所述乘员的生命体征指标，所述生命体征指标包括以下至少一项：呼吸频率、血压、心率；将所述生命体征指标发送给紧急呼叫中心。
生命体征指标可以基于生理特征传感器采集的生理特征传感信息来确定，示例性地，以生命特征传感器为毫米波雷达为例，毫米波雷达对呼吸心率监测的原理是利用雷达发 射电磁波，然后检测回波信号的频率来实现对乘员呼吸心率的检测，在该示例中，生命特征传感信息即为毫米波雷达的回波信号。毫米波雷达能够通过测量回波信号相位的变化来检测人体微小的振动和位移，在一个示例中，可以基于对胸腔振动幅度的检测，来确定心跳和呼吸的频率。
在确定生命体征指标后，可以将所述生命体征指标发送给紧急呼叫中心，以便紧急呼叫中心合理地调度救援力量，对乘客进行针对性救援。
在一种可能的实现方式中，所述方法还包括：基于确定的所述乘员的流血情况、异常身体姿态、生命体征指标中的至少一项，确定舱内乘员的受伤严重级别；将所述受伤严重级别发送给紧急呼叫中心。
受伤严重级别用来表征乘员受伤的严重程度，该级别例如可以分为0-10级，级别越高，受伤程度越严重，0级指示乘员未受伤。
受伤严重级别可以有一个或多个，例如，可以用一个受伤级别来综合表征乘员的流血情况、异常身体姿态、生命体征指标等严重程度，或者，可以用多个严重级别分别表征乘员受伤严重程度，例如，可以预设流血严重级别、骨折严重级别、生命体征衰弱级别等等。
流血严重级别可以依据流血的面积和流血部位来确定，流血的面积与流血严重级别正相关，流血的部位处于头部、腹部等要害部位则流血严重级别越高。骨折严重级别与人体骨骼的弯折程度正相关，与骨折的骨骼数量正相关，骨折部位处于头部等要害部位则流血严重级别越高。生命体征衰弱级别则与生命体征指标负相关，呼吸频率、血压、心率越低表征生命体征衰弱级别越高，
在用一个受伤严重级别来综合表征乘员的流血情况、异常身体姿态、生命体征指标等严重程度的情况下，可以对乘员的流血情况、异常身体姿态、生命体征指标的严重级别进行加权平均，来得到一个综合的受伤严重级别；或者，可以根据乘员的流血情况、异常身体姿态、生命体征指标中最严重的一项来确定受伤严重级别。
在本公开实施例中，通过基于确定的所述乘员的流血情况、异常身体姿态、生命体征指标中的至少一项，确定舱内乘员的受伤严重级别，将所述受伤严重级别发送给紧急呼叫中心。由此，紧急呼叫中心能够根据受伤严重等级，优先抢救受伤严重等级高的呼叫方的乘员，减少或者省略掉询问过程，提供更快速的救援，减少人身财产损失。
下面对本公开实施例的一个应用场景进行说明。在该应用场景中，事故发生后，紧急呼叫被触发，然后获取舱内乘员的影像信息，检测到舱内乘员没有明显的流血情况，且身体姿态正常，则判断伤亡情况为轻。接通紧急呼叫后，呼叫中心与车内人员简单确认后，确认并不需要额外救助。
下面对本公开实施例的又一个应用场景进行说明。在该应用场景中，事故发生后，紧急呼叫被触发，然后获取舱内乘员的影像信息，检测到舱内乘员体表大量流血，肢体静止且呼吸微弱，确定受伤严重级别为高级，将受伤严重级别发送给紧急呼叫中心。紧急呼叫直接派出救护车到事故的地点，同时呼叫中心的人员与车内进行通话进一步了解 情况。
可以理解，本公开提及的上述各个方法实施例，在不违背原理逻辑的情况下，均可以彼此相互结合形成结合后的实施例，限于篇幅，本公开不再赘述。本领域技术人员可以理解，在具体实施方式的上述方法中，各步骤的具体执行顺序应当以其功能和可能的内在逻辑确定。
此外，本公开还提供了用于车辆的向紧急呼叫中心发送信息的装置、电子设备、计算机可读存储介质、程序，上述均可用来实现本公开提供的任一种信息发送方法，相应技术方案和描述和参见方法部分的相应记载，不再赘述。
图2示出根据本公开实施例的信息发送装置的框图，如图2所示，所述装置20包括：
影像信息获取单元21，用于响应于紧急呼叫被触发，获取舱内乘员的影像信息；
流血情况检测单元22，用于基于所述影像信息，检测舱内乘员的流血情况；
流血情况发送单元23，用于响应于检测到流血情况，将所述流血情况发送给紧急呼叫中心。
在一种可能的实现方式中，所述流血情况检测单元，包括：
乘员检测子单元，用于对所述影像信息进行人脸检测和/或人体检测，确定出所述舱内的乘员；
第一流血情况确定子单元，用于在所述乘员的面部和/或体表进行血液检测，确定舱内乘员的流血情况。
在一种可能的实现方式中，所述流血情况检测单元，用于基于血液的颜色信息和血流的形状信息，基于所述影像信息检测所述乘员是否流血。
在一种可能的实现方式中，所述流血情况检测单元，包括：
体表区域检测子单元，用于基于所述影像信息，检测所述舱内的乘员的体表区域；
检测区域划分子单元，用于将所述乘员的体表区域划分为多个检测区域；
区域检测结果确定子单元，用于在每个所述检测区域中检测血液信息，得到各所述检测区域的区域检测结果；
第二流血情况确定子单元，用于基于各所述检测区域的区域检测结果，确定所述乘员的流血情况。
在一种可能的实现方式中，所述体表区域检测子单元，用于基于所述影像信息，检测舱内的乘员的人脸表面区域；
所述检测区域划分子单元，用于将所述乘员的人脸表面区域划分为多个检测区域。
在一种可能的实现方式中，所述区域检测结果确定子单元，用于基于每个检测区域中血流的形状和面积，确定每个所述检测区域存在流血情况的第一置信度；确定各相邻的检测区域之间是否存在相接的血流；响应于确定所述检测区域中的第一检测区域与相邻的第二检测区域存在相接的血流，将所述多个第一检测区域和所述第二检测区域的置信度升高为第二置信度；
所述第二流血情况确定子单元，用于在所述第一置信度或所述第二置信度超过置信 度阈值的情况下，确定所述乘员流血；基于所述每个检测区域中的血流的面积，确定流血的严重程度，所述流血的严重程度与所述各所述检测区域的血流的面积和正相关。
在一种可能的实现方式中，所述流血情况检测单元，包括：
流血部位检测子单元，用于响应于基于所述影像信息检测到所述舱内的乘员流血，确定流血的身体部位以及血流的方向；
出血部位检测子单元，用于基于所述流血的身体部位以及血流的方向，将血流的起始端所在的身体部位，作为出血部位。
在一种可能的实现方式中，所述装置还包括：
身体姿态确定单元，用于根据所述影像信息，确定所述舱内乘员的身体姿态；
异常身体姿态确定单元，用于在所述身体姿态为预设的异常身体姿态、且所述异常身体姿态的持续时长超过设定时长的情况下，确定所述舱内乘员的身体姿态为异常身体姿态。
在一种可能的实现方式中，所述装置还包括：
骨折状况检测单元，用于在确定所述舱内乘员的身体姿态为预设的骨折姿态的情况下，确定所述舱内乘员存在骨折状况。
在一种可能的实现方式中，所述装置还包括：
生命体征指标确定单元，用于基于所述影像信息确定所述乘员的生命体征指标，所述生命体征指标包括以下至少一项：
呼吸频率、血压、心率；
生命体征指标发送单元，用于将所述生命体征指标发送给紧急呼叫中心。
在一种可能的实现方式中，所述装置还包括：
受伤严重级别确定单元，用于基于确定的所述乘员的流血情况、异常身体姿态、生命体征指标中的至少一项，确定舱内乘员的受伤严重级别；
受伤严重级别发送单元，用于将所述受伤严重级别发送给紧急呼叫中心。
在一些实施例中，本公开实施例提供的装置具有的功能或包含的模块可以用于执行上文方法实施例描述的方法，其具体实现和技术效果可以参照上文方法实施例的描述，为了简洁，这里不再赘述。
本公开实施例还提出一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述方法。计算机可读存储介质可以是易失性或非易失性计算机可读存储介质。
本公开实施例还提出一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为调用所述存储器存储的指令，以执行上述方法。
本公开实施例还提供了一种计算机程序产品，包括计算机可读代码，或者承载有计算机可读代码的非易失性计算机可读存储介质，当所述计算机可读代码在电子设备的处理器中运行时，所述电子设备中的处理器执行上述方法。
电子设备可以被提供为终端、服务器或其它形态的设备。
图3示出根据本公开实施例的一种电子设备800的框图。例如，电子设备800可以是移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备，个人数字助理等终端。
参照图3，电子设备800可以包括以下一个或多个组件：处理组件802，存储器804，电源组件806，多媒体组件808，音频组件810，输入/输出(I/O)的接口812，传感器组件814，以及通信组件816。
处理组件802通常控制电子设备800的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理组件802可以包括一个或多个处理器820来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件802可以包括一个或多个模块，便于处理组件802和其他组件之间的交互。例如，处理组件802可以包括多媒体模块，以方便多媒体组件808和处理组件802之间的交互。
存储器804被配置为存储各种类型的数据以支持在电子设备800的操作。这些数据的示例包括用于在电子设备800上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器804可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电源组件806为电子设备800的各种组件提供电力。电源组件806可以包括电源管理系统，一个或多个电源，及其他与为电子设备800生成、管理和分配电力相关联的组件。
多媒体组件808包括在所述电子设备800和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件808包括一个前置摄像头和/或后置摄像头。当电子设备800处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜系统或具有焦距和光学变焦能力。
音频组件810被配置为输出和/或输入音频信号。例如，音频组件810包括一个麦克风(MIC)，当电子设备800处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器804或经由通信组件816发送。在一些实施例中，音频组件810还包括一个扬声器，用于输出音频信号。
I/O接口812为处理组件802和外围接口模块之间提供接口，上述外围接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件814包括一个或多个传感器，用于为电子设备800提供各个方面的状态评估。例如，传感器组件814可以检测到电子设备800的打开/关闭状态，组件的相对定位，例如所述组件为电子设备800的显示器和小键盘，传感器组件814还可以检测电子设备800或电子设备800一个组件的位置改变，用户与电子设备800接触的存在或不存在，电子设备800方位或加速/减速和电子设备800的温度变化。传感器组件814可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件814还可以包括光传感器，如互补金属氧化物半导体(CMOS)或电荷耦合装置(CCD)图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件814还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件816被配置为便于电子设备800和其他设备之间有线或无线方式的通信。电子设备800可以接入基于通信标准的无线网络，如无线网络(WiFi)，第二代移动通信技术(2G)或第三代移动通信技术(3G)，或它们的组合。在一个示例性实施例中，通信组件816经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，所述通信组件816还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，电子设备800可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器804，上述计算机程序指令可由电子设备800的处理器820执行以完成上述方法。
图4示出根据本公开实施例的一种电子设备1900的框图。例如，电子设备1900可以被提供为一服务器。参照图4，电子设备1900包括处理组件1922，其进一步包括一个或多个处理器，以及由存储器1932所代表的存储器资源，用于存储可由处理组件1922的执行的指令，例如应用程序。存储器1932中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件1922被配置为执行指令，以执行上述方法。
电子设备1900还可以包括一个电源组件1926被配置为执行电子设备1900的电源管理，一个有线或无线网络接口1950被配置为将电子设备1900连接到网络，和一个输入输出(I/O)接口1958。电子设备1900可以操作基于存储在存储器1932的操作系统，例如微软服务器操作系统(Windows Server TM)，苹果公司推出的基于图形用户界面操作系统(Mac OS X TM)，多用户多进程的计算机操作系统(Unix TM),自由和开放原代码的类Unix操作系统(Linux TM)，开放原代码的类Unix操作系统(FreeBSD TM)或类似。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器1932，上述计算机程序指令可由电子设备1900的处理组件1922执行以 完成上述方法。
本公开可以是系统、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本公开的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是(但不限于)电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本公开操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，所述编程语言包括面向对象的编程语言—诸如Smalltalk、C++等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本公开的各个方面。
这里参照根据本公开实施例的方法、装置(系统)和计算机程序产品的流程图和/或框图描述了本公开的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处 理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本公开的多个实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
该计算机程序产品可以具体通过硬件、软件或其结合的方式实现。在一个可选实施例中，所述计算机程序产品具体体现为计算机存储介质，在另一个可选实施例中，计算机程序产品具体体现为软件产品，例如软件开发包(Software Development Kit，SDK)等等。
以上已经描述了本公开的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术的改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。

Claims (15)

  1. 一种用于车辆的向紧急呼叫中心发送信息的方法，其特征在于，包括：
    响应于紧急呼叫被触发，获取舱内乘员的影像信息；
    基于所述影像信息，检测舱内乘员的流血情况；
    响应于检测到流血情况，将所述流血情况发送给紧急呼叫中心。
  2. 根据权利要求1所述方法，其特征在于，所述基于所述影像信息，检测舱内乘员的流血情况，包括：
    对所述影像信息进行人脸检测和/或人体检测，确定出所述舱内的乘员；
    在所述乘员的面部和/或体表进行血液检测，确定舱内乘员的流血情况。
  3. 根据权利要求1或2任一所述方法，其特征在于，所述基于所述影像信息，检测舱内乘员的流血情况，包括：
    基于血液的颜色信息和血流的形状信息，基于所述影像信息检测所述乘员是否流血。
  4. 根据权利要求1-3任一所述方法，其特征在于，所述基于所述影像信息，检测舱内乘员的流血情况，包括：
    基于所述影像信息，检测所述舱内的乘员的体表区域；
    将所述乘员的体表区域划分为多个检测区域；
    在每个所述检测区域中检测血液信息，得到各所述检测区域的区域检测结果；
    基于各所述检测区域的区域检测结果，确定所述乘员的流血情况。
  5. 根据权利要求4所述的方法，其特征在于，所述基于所述影像信息，检测所述舱内的乘员的体表区域，包括：
    基于所述影像信息，检测舱内的乘员的人脸表面区域；
    所述将所述乘员的体表区域划分为多个检测区域，包括：
    将所述乘员的人脸表面区域划分为多个检测区域。
  6. 根据权利要求4或5所述方法，其特征在于，所述在每个所述检测区域中检测血液信息，得到各所述检测区域的区域检测结果，包括：
    基于每个检测区域中血流的形状和面积，确定每个所述检测区域存在流血情况的第一置信度；
    确定各相邻的检测区域之间是否存在相接的血流；
    响应于确定所述检测区域中的第一检测区域与相邻的第二检测区域存在相接的血流，将所述多个第一检测区域和所述第二检测区域的置信度升高为第二置信度；
    所述基于各所述检测区域的区域检测结果，确定所述乘员的流血情况，包括：
    在所述第一置信度或所述第二置信度超过置信度阈值的情况下，确定所述乘员流血；
    基于所述每个检测区域中的血流的面积，确定流血的严重程度，所述流血的严重程度与所述各所述检测区域的血流的面积和正相关。
  7. 根据权利要求1-6任一所述方法，其特征在于，所述基于所述影像信息，检测舱内乘员的流血情况，包括：
    响应于基于所述影像信息检测到所述舱内的乘员流血，确定流血的身体部位以及血流的方向；
    基于所述流血的身体部位以及血流的方向，将血流的起始端所在的身体部位，作为出血部位。
  8. 根据权利要求1-7任一所述方法，其特征在于，所述方法还包括：
    根据所述影像信息，确定所述舱内乘员的身体姿态；
    在所述身体姿态为预设的异常身体姿态、且所述异常身体姿态的持续时长超过设定时长的情况下，确定所述舱内乘员的身体姿态为异常身体姿态。
  9. 根据权利要求8所述方法，其特征在于，所述方法还包括：
    在确定所述舱内乘员的身体姿态为预设的骨折姿态的情况下，确定所述舱内乘员存在骨折状况。
  10. 根据权利要求1-9任一所述方法，其特征在于，所述方法还包括：
    基于所述影像信息确定所述乘员的生命体征指标，所述生命体征指标包括以下至少一项：
    呼吸频率、血压、心率；
    将所述生命体征指标发送给紧急呼叫中心。
  11. 根据权利要求1-10任一所述方法，其特征在于，所述方法还包括：
    基于确定的所述乘员的流血情况、异常身体姿态、生命体征指标中的至少一项，确定舱内乘员的受伤严重级别；
    将所述受伤严重级别发送给紧急呼叫中心。
  12. 一种用于车辆的向紧急呼叫中心发送信息的装置，其特征在于，包括：
    影像信息获取单元，用于响应于紧急呼叫被触发，获取舱内乘员的影像信息；
    流血情况检测单元，用于基于所述影像信息，检测舱内乘员的流血情况；
    流血情况发送单元，用于响应于检测到流血情况，将所述流血情况发送给紧急呼叫中心。
  13. 一种电子设备，其特征在于，包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为调用所述存储器存储的指令，以执行权利要求1至11中任意一项所述的方法。
  14. 一种计算机可读存储介质，其上存储有计算机程序指令，其特征在于，所述计算机程序指令被处理器执行时实现权利要求1至11中任意一项所述的方法。
  15. 一种计算机程序产品，包括计算机可读代码，或者承载有计算机可读代码的非易失性计算机可读存储介质，当所述计算机可读代码在电子设备的处理器中运行时，所述电子设备中的处理器执行用于实现权利要求1-11中的任一权利要求所述的方法。
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