CN104851295B - Obtain the method and system of traffic information - Google Patents
Obtain the method and system of traffic information Download PDFInfo
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- CN104851295B CN104851295B CN201510266525.4A CN201510266525A CN104851295B CN 104851295 B CN104851295 B CN 104851295B CN 201510266525 A CN201510266525 A CN 201510266525A CN 104851295 B CN104851295 B CN 104851295B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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Abstract
Embodiment of the disclosure provides a kind of method and system for obtaining traffic information.The method for obtaining traffic information includes：The image and/or video data of road where the vehicle information data of collection vehicle and the vehicle；Denoising is carried out to described image and/or video data；The image and/or video data after denoising are analyzed, the traffic information of the road residing for the vehicle is obtained；And send the traffic information.
Description
Technical Field
Embodiments of the present disclosure relate to a method and system for acquiring traffic information, and more particularly, to a method and system for acquiring traffic information based on images and/or videos.
Background
The first technology of the urban road condition mining system is realized by acquiring traffic information, mining data, extracting traffic state information from the traffic information, and fusing the traffic state information. Under the general condition, data needs to be periodically acquired and processed, urban history and current road conditions are obtained through related technologies of data mining, and then traffic congestion is relieved and prevented through modes such as traffic guidance and the like, and finally, reasonable distribution of traffic flow on each road section of a road network is realized, and urban traffic efficiency is improved.
At present, the main sources of traffic information data are three types: physical sensors, floating cars and video detectors. An apparatus for acquiring data via a physical sensor comprising: coil detectors, ultrasonic detectors, infrared detectors, and the like. Most of the sensors are fixed sensors and collect the traffic condition of vehicles in a fixed position or area. The floating car is a common vehicle provided with a positioning and wireless communication device, and the vehicle can exchange information with a traffic information center. When the floating vehicle runs on the road, the information of longitude and latitude coordinates, instantaneous speed, direction, return time and the like of the vehicle can be reported in real time. The video detector is fixed point detection equipment, and can detect the traffic parameters of a plurality of lanes simultaneously by carrying out image analysis on the collected traffic images.
After the data from different sources are collected, information such as traffic flow, average speed, headway, time occupancy, space occupancy, vehicle density and the like of each road in the city can be calculated through technical means such as data fusion, big data analysis and the like, and the traffic state of each road is classified. Traffic conditions can be generally classified as: smooth, slow, congested and serious congestion. Finally, the current traffic condition can be displayed to the user through broadcasting, webpage display and other modes, and the vehicle is guided through navigation and other modes, so that the effects of avoiding congestion and improving the operation efficiency are achieved.
Chinese patent application No. 200710043538.0 discloses a visual intelligent traffic management system and its implementation method, which comprises: electronic license plate, remote vehicle recognition device, data storage device and communication interface device. The remote vehicle recognition device is uninterruptedly installed on the road, recognizes the vehicle information in the electronic license plate of the vehicle, stores the vehicle information in the data storage device, is in communication connection with the communication interface device, uploads the vehicle information to the urban traffic command center in real time, and carries out remote positioning and tracking, remote speed measurement, traffic flow statistics of each road section and automatic dredging under the busy state on the vehicle.
Chinese patent application No. 03134351.1 relates to a vehicle traffic management method based on GPS technology, which encodes geographic information transmitted by a wireless transmitting and receiving unit, and then decodes the geographic information received by the wireless transmitting and receiving unit. The encoding method is that the vehicle number of a certain vehicle is used as a code, and the confirmed geographical position information of other vehicle codes can be received and processed, and the distance information is displayed. The automobile traffic management method based on the GPS technology can realize that the distance and the distance change of the vehicles before and after running on the road can be known at any time, and is not influenced by the environment.
The traffic data is collected through a physical sensor or a video detection technology, and the physical and video sensors are required to be uniformly installed on various roads, intersections and the like in a city. Therefore, the method has the defects of high installation and maintenance cost, small coverage range, capability of detecting data of fixed positions only and the like. The fixed sensor is difficult to be popularized in a large scale due to the restriction of factors such as manpower, capital and the like.
The floating car technology can acquire the running state of a vehicle in real time, and is the most commonly used traffic information acquisition method at present. However, in practical application, the traffic condition is affected by various factors such as traffic flow, headway, vehicle density and the like, and the method can only acquire data such as coordinates, speed and the like of a single vehicle, so that the overall condition of the road is difficult to evaluate. And because a device (such as a GPS device) for specifically acquiring the coordinates and the speed of the vehicle needs to be installed, the road condition is difficult to be accurately provided in real time under the condition of limited installation amount. Moreover, the driving speed of the vehicle is greatly influenced by signal facilities such as traffic lights, and the single speed data cannot reflect the traffic light state in front of the vehicle. For vehicles traveling at low speeds, it is difficult to distinguish whether it is caused by a red light or congestion. Therefore, the classification accuracy for the road congestion state is low.
Disclosure of Invention
Embodiments of the present disclosure are directed to a method and system for acquiring traffic information, which overcome the above disadvantages.
According to an aspect of the present disclosure, there is provided a method for acquiring traffic information, the method including: collecting vehicle information data of a vehicle and image and/or video data of a road where the vehicle is located; denoising the image and/or video data; analyzing the denoised image and/or video data to obtain road condition information of a road where the vehicle is located; and sending the road condition information.
In one embodiment, the vehicle information data includes data of coordinates of the vehicle.
In yet another embodiment, the vehicle information data further comprises data of the speed and/or direction of the vehicle.
In yet another embodiment, denoising the image and/or video data comprises: identifying vehicles, signal lights and traffic signs in the images and/or videos; obtaining a number of vehicles in the image and/or video; and extracting the state of the signal lamp and the meaning of the traffic sign.
In yet another embodiment, identifying vehicles, signal lights and traffic signs in the image and/or video comprises: and identifying vehicles, signal lamps and traffic signs in the images and/or videos by extracting the histogram of direction gradient and the haar characteristics of the images and/or videos.
In yet another embodiment, obtaining the number of vehicles in the image and/or video comprises: and obtaining the number of vehicles in the image and/or the video through spatial matrix operation.
In yet another embodiment, extracting the status of the signal lights and the meaning of the traffic signs includes: and extracting the state of the signal lamp and the meaning of the traffic sign through a pattern recognition algorithm.
In another embodiment, analyzing the denoised image and/or video data to obtain road condition information of a road where the vehicle is located includes: determining the vehicle density of the road on which the vehicle is positioned according to the number of vehicles counted from the image and/or video data in unit time and unit road area.
In yet another embodiment, an average vehicle density of the roadway is determined based on the vehicle density of the roadway obtained from each vehicle.
In another embodiment, analyzing the denoised image and/or video data to obtain road condition information of a road where the vehicle is located includes: and determining the average passing speed of the road on which the vehicle is positioned according to the vehicle information data of each vehicle.
In another embodiment, analyzing the denoised image and/or video data to obtain road condition information of a road where the vehicle is located includes: and determining the actual passing speed of the road where the vehicle is located according to the red light duration, the green light duration and the vehicle information data of the signal lamp.
In yet another embodiment, the average traffic speed of the road on which each vehicle is located is determined from the actual traffic speed of the vehicle.
In yet another embodiment, the method further comprises: determining the congestion level of each road according to the road condition information of each road; and generating navigation guidance information based on the congestion level of each road.
According to another aspect of the present disclosure, there is provided a system for acquiring traffic information, including: a vehicle information acquisition unit configured to acquire vehicle information data of a vehicle; the image video acquisition unit is configured to acquire images and/or video data of a road where the vehicle is located; a data denoising processing unit configured to denoise the image and/or video data; the data analysis unit is configured to analyze the denoised image and/or video data and acquire road condition information of a road where the vehicle is located; and an information transmitting unit configured to transmit the traffic information.
In yet another embodiment, the vehicle information data comprises data of coordinates of the vehicle.
In yet another embodiment, the vehicle information data further comprises data of the speed and/or direction of the vehicle.
In yet another embodiment, denoising the image and/or video data comprises: identifying vehicles, signal lights and traffic signs in the images and/or videos; obtaining a number of vehicles in the image and/or video; and extracting the state of the signal lamp and the meaning of the traffic sign.
In yet another embodiment, identifying vehicles, signal lights and traffic signs in the image and/or video comprises: and identifying vehicles, signal lamps and traffic signs in the images and/or videos by extracting the histogram of direction gradient and the haar characteristics of the images and/or videos.
In yet another embodiment, obtaining the number of vehicles in the image and/or video comprises: and obtaining the number of vehicles in the image and/or the video through spatial matrix operation.
In yet another embodiment, extracting the status of the signal lights and the meaning of the traffic signs includes: and extracting the state of the signal lamp and the meaning of the traffic sign through a pattern recognition algorithm.
In another embodiment, analyzing the denoised image and/or video data, and acquiring road condition information of a road where the vehicle is located includes: determining the vehicle density of the road on which the vehicle is positioned according to the number of vehicles counted from the image and/or video data in unit time and unit road area.
In yet another embodiment, an average vehicle density of the roadway is determined based on the vehicle density of the roadway obtained from each vehicle.
In another embodiment, analyzing the denoised image and/or video data, and acquiring road condition information of a road where the vehicle is located includes: and determining the average passing speed of the road on which the vehicle is positioned according to the vehicle information data of each vehicle.
In another embodiment, analyzing the denoised image and/or video data, and acquiring road condition information of a road where the vehicle is located includes: and determining the actual passing speed of the road where the vehicle is located according to the red light duration, the green light duration and the vehicle information data of the signal lamp.
In yet another embodiment, the average traffic speed of the road on which each vehicle is located is determined from the actual traffic speed of the vehicle.
In yet another embodiment, the data analysis unit is further configured to: determining the congestion level of each road according to the road condition information of each road; and generating navigation guidance information based on the congestion level of each road.
Embodiments of the present disclosure are capable of simultaneously capturing coordinates, speed, and image and/or video data of a vehicle. Road information such as vehicle density is obtained by analyzing the image and/or video data, and the actual traffic speed of the road is obtained by combining the speed of the collected vehicle with signal light information obtained by analyzing the image and/or video data. The vehicle-mounted device (for example, a mobile phone) provided by the embodiment of the disclosure is convenient to mount and can be mounted on a plurality of vehicles at the same time. By comprehensively analyzing the various data of the vehicles, the accuracy of road condition classification can be greatly improved, and the defects of low real-time rate, poor accuracy and the like of the conventional road condition information acquisition system are further overcome.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
fig. 1 schematically illustrates a flowchart of a method of acquiring traffic information according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a method of calculating an average vehicle density of a roadway according to an embodiment of the present disclosure;
fig. 3 schematically illustrates a flow chart of a method of calculating an average traffic speed of a road according to an embodiment of the present disclosure; and
fig. 4 schematically illustrates a block diagram of a system for acquiring road condition information according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, various exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. It should be noted that the figures and description relate to exemplary preferred embodiments only. It should be noted that from the following description, alternative embodiments of the structures and methods disclosed herein are readily contemplated and may be used without departing from the principles of the present disclosure as claimed.
It should be understood that these exemplary embodiments are given solely for the purpose of enabling those skilled in the art to better understand and to practice the present disclosure, and are not intended to limit the scope of the present disclosure in any way.
The terms "including," comprising, "and the like as used herein, are to be construed as open-ended terms, i.e.," including/including but not limited to. The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment". The term "road" as used herein may refer not only to "a single lane", but also to "a road containing a plurality of lanes traveling in the same direction" or other vehicle passing units to which the embodiments of the present disclosure may be applied. The terms "image" and "video" as used herein are generally replaceable in that a video may be viewed as a series of images arranged according to a time axis, and an image may be viewed as a frame in a video. Thus, "image" and "video" may be used interchangeably herein unless the context clearly dictates otherwise. Relevant definitions for other terms will be given in the following description.
Fig. 1 schematically illustrates a flowchart of a method 100 of acquiring traffic information according to an embodiment of the present disclosure. As shown in fig. 1, the method 100 includes steps S101 to S104.
In step S101, vehicle information data of a vehicle and image and/or video data of a road on which the vehicle is located are collected. According to an embodiment of the present disclosure, the vehicle information data includes data of coordinates of the vehicle, for example, coordinates of the vehicle collected using a GPS, a base station positioning device, or the like. By examining the continuous vehicle coordinates, it is also possible to further derive data on the speed and direction of the vehicle. According to an embodiment of the present disclosure, the vehicle information data includes data of coordinates, speed, and direction of the vehicle. For example, in some GPS devices, data may be obtained directly for the coordinates, speed and direction of the vehicle. The image and/or video data may be captured by an image and/or video capture device mounted on the vehicle, such as a cell phone, a tachograph, a single/binocular camera, etc.
In step S102, denoising processing is performed on the image and/or video data. Under the normal condition, due to the limitation of network bandwidth and the like and the improvement of the performance of a data denoising processing unit in the vehicle-mounted acquisition equipment, the acquired image and/or video data can be denoised on a vehicle, and the transmission through a network is facilitated. In an embodiment of the present disclosure, the processing of image and/or video data comprises: identifying vehicles, signal lights and traffic signs in the images and/or videos; obtaining a number of vehicles in the image and/or video; and extracting the state of the signal lamp and the meaning of the traffic sign. In an embodiment of the present disclosure, vehicles, signal lights and traffic signs in the images and/or videos are identified by extracting histogram of oriented gradient (HoG), Haar (Haar) features of the images and/or videos. In an embodiment of the present disclosure, the number of vehicles in the image and/or video is obtained by a spatial matrix operation. The distance and angle between the vehicles can be further obtained by spatial matrix operation, if desired. In the embodiments of the present disclosure, the state of the signal lamp and the meaning of the traffic sign are extracted through a pattern recognition algorithm. For example, the status of the traffic light may be red, green. For example, the traffic sign may be a speed limit of 60 km/h. In addition, the road condition information data and the data extracted from the image and/or video data can be compressed, thereby facilitating network transmission.
In step S103, the denoised image and/or video data is analyzed to obtain road condition information of the road where the vehicle is located.
Fig. 2 schematically illustrates a flow chart of a method 200 of calculating an average vehicle density of a road according to an embodiment of the disclosure. According to an embodiment of the present disclosure, as shown in fig. 2, the method 200 includes a step S201. In step S201, the vehicle density of the road on which the vehicle is located is determined based on the number of vehicles counted from the image and/or video data in a unit time and a unit road area. The specific calculation method can be as follows. The vehicle density is an instantaneous value that varies not only with time but also with the measurement interval. Therefore, the instantaneous density is often expressed as an average value over a certain period of time. Specifically, first, the number of vehicles in the image and/or video data ahead of a certain vehicle over a certain time (for example, 5 minutes) is counted. In addition, data on the size of the road (e.g., road length and width) or the coordinates of the road is obtained from known map data or other data. Also, the length of the road in front of the vehicle, on which the number of vehicles is counted, may be determined in conjunction with the vehicle coordinates obtained from the vehicle information data, and the density of vehicles within the counted road length may be obtained based thereon. For example, the vehicle density of the road can be derived by calculating the average number of vehicles in a certain period within a certain distance L ahead of the vehicle. The specific calculation formula is as follows:
wherein N isvThe total number of vehicles within the distance L in front of the vehicle; w is the road width; t is the statistical duration; ρ is the vehicle density of the road.
According to a further preferred embodiment of the present disclosure, as shown in fig. 2, the method 200 may further include a step S202. In step S202, an average vehicle density of the road is determined from the vehicle density of the road acquired from each vehicle. For example, data may be received from n vehicles traveling on the same road and the vehicle densities obtained by all of these vehicles may be averaged by the number of vehicles n, which may allow a more accurate assessment of the vehicle density on the road.
According to the embodiment of the disclosure, the average traffic speed of the road on which each vehicle is located is determined according to the vehicle information data of the vehicle. The specific calculation formula is as follows:
wherein, ViIs the passing speed of each vehicle obtained from the vehicle information data; n represents the number of collection vehicles;representing the average traffic speed of the road. As described above, after statistical averaging of a plurality of data, the obtained data is more accurate and reliable.
Fig. 3 schematically illustrates a flow chart of a method 300 of calculating an average traffic speed of a road according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, as shown in fig. 3, the method 300 may include step S301 and step S302. In step S301, a distance traveled by the vehicle in a traffic light period is determined according to the speed of the vehicle and the duration of the green light and the red light. In step S302, the road of the vehicle is setAnd dividing the distance by the duration of the green light to determine the actual passing speed of the vehicle. As described in more detail below. First, by analyzing the state of the signal lamp in front, it is possible to obtain the red light duration T of the signal lamp in a certain period (for example, 5 minutes)LRDuration T of green lightLG. From the speed data (or speed data obtained from continuous coordinate data) V recorded in the vehicle information data, it can be derived that the time (equal to T) during that periodLR+TLG) The distance traveled and dividing it by the duration T of the green lightLGThat is, the actual passing speed V can be obtainedL. The specific calculation formula is as follows:
VL＝V*(TLR+TLG)/TLG(3)
according to a further preferred embodiment of the present disclosure, as shown in fig. 3, the method 300 may further include step S303. In step S303, the actual traffic speed of each vehicle is averaged, and the average traffic speed of the road on which the vehicle is located is determined. The specific calculation formula is as follows:
wherein,the actual passing speed of each vehicle is calculated by a formula (3); n represents the number of collection vehicles;representing the average traffic speed of the road.
According to the embodiment of the disclosure, the congestion level of each road is determined according to the road condition information of each road; and generates navigation guidance information based on the congestion level of each road. After the average traffic speed and the vehicle density of each road are calculated according to the method, the congestion degree of each road can be comprehensively judged according to the road grade and the historical condition. The congestion degree of a road can be divided into 4 grades: smooth, slow, congested and severe congestion. In addition, navigation guide information is generated according to the obtained congestion levels of all the roads, so that the vehicles are guided to run on the roads with lower road congestion levels (for example, smooth roads and the like), and the traffic congestion condition is relieved.
In step S104, the traffic information is sent. For example, the road congestion degree and the navigation guidance information may be transmitted to the mobile terminal through a network, radio, or the like. The user can receive the road condition information and the navigation guidance information by using the mobile terminal including a mobile phone, a radio and the like at any time. After acquiring the road condition information, the user can plan the route of the user. For example, the user may receive the traffic information at the fixed terminal, and prepare his or her trip in advance.
Fig. 4 schematically illustrates a block diagram of a system 400 for acquiring traffic information according to an embodiment of the present disclosure. As shown in fig. 4, the system 400 includes: a vehicle information acquisition unit 401, an image video acquisition unit 402, a data denoising processing unit 403, a data analysis unit 404, and an information transmission unit 405. The vehicle information collection unit 401 is configured to collect vehicle information data of the vehicle. The image video capturing unit 402 is configured to capture image and/or video data of the road on which the vehicle is located. The data denoising processing unit 403 is configured to denoise the image and/or video data. The data analysis unit 404 is configured to analyze the denoised image and/or video data to obtain road condition information of a road where the vehicle is located. The information transmitting unit 405 is configured to transmit the traffic information. Note that the units described herein may be implemented in a single device, or may be implemented in different devices separately, or may be implemented in part in a single device and in part in different devices, and thus the above embodiments are not intended to limit the physical forms in which the systems are embodied. In a preferred embodiment of the present disclosure, the vehicle information acquisition unit 401, the image video acquisition unit 402, and the data denoising processing unit 403 of the system 400 are implemented on a vehicle, and the data analysis unit 404 and the information transmission unit 405 are implemented in a server, between which information transmission is performed through a communication medium such as a network.
According to an embodiment of the present disclosure, the vehicle information data includes data of coordinates of the vehicle. According to an embodiment of the present disclosure, the vehicle information data further comprises data of a speed and/or a direction of the vehicle.
According to an embodiment of the present disclosure, denoising the image and/or video data includes: identifying vehicles, signal lights and traffic signs in the images and/or videos; obtaining a number of vehicles in the image and/or video; and extracting the state of the signal lamp and the meaning of the traffic sign.
According to an embodiment of the present disclosure, identifying vehicles, signal lights and traffic signs in the image and/or video comprises: and identifying vehicles, signal lamps and traffic signs in the images and/or videos by extracting the histogram of direction gradient and the haar characteristics of the images and/or videos.
According to an embodiment of the present disclosure, obtaining the number of vehicles in the image and/or video comprises: and obtaining the number of vehicles in the image and/or the video through spatial matrix operation.
According to an embodiment of the present disclosure, extracting the state of the signal lamp and the meaning of the traffic sign includes: and extracting the state of the signal lamp and the meaning of the traffic sign through a pattern recognition algorithm.
According to the embodiment of the disclosure, analyzing the denoised image and/or video data, and acquiring the road condition information of the road where the vehicle is located comprises: determining the vehicle density of the road on which the vehicle is positioned according to the number of vehicles counted from the image and/or video data in unit time and unit road area. According to an embodiment of the present disclosure, an average vehicle density of a road is determined from vehicle densities of the road acquired from each vehicle.
According to the embodiment of the disclosure, analyzing the denoised image and/or video data, and acquiring the road condition information of the road where the vehicle is located comprises: and determining the average passing speed of the road on which the vehicle is positioned according to the vehicle information data of each vehicle.
According to the embodiment of the disclosure, analyzing the denoised image and/or video data, and acquiring the road condition information of the road where the vehicle is located comprises: and determining the actual passing speed of the road where the vehicle is located according to the red light duration, the green light duration and the vehicle information data of the signal lamp. According to an embodiment of the present disclosure, an average traffic speed of a road on which each vehicle is located is determined from an actual traffic speed of the vehicle.
According to an embodiment of the present disclosure, the data analysis unit 404 is further configured to: determining the congestion level of each road according to the road condition information of each road; and generating navigation guidance information based on the congestion level of each road.
It will be apparent to those skilled in the art that the various elements or steps of the disclosure described above may be implemented by a general purpose computing device, centralized on a single computing device or distributed across a network of multiple computing devices, or alternatively, they may be implemented by program code executable by a computing device, such that it may be stored in a memory device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. As such, the present disclosure is not limited to any specific combination of hardware and software.
The above description is intended only as an alternative embodiment of the present disclosure and is not intended to limit the present disclosure, which may be modified and varied by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (24)
1. A method for acquiring road condition information comprises the following steps:
acquiring vehicle information data of a vehicle and image and/or video data of a road where the vehicle is located, wherein the image and/or video data are acquired through an image and/or video acquisition device arranged on the vehicle;
denoising the image and/or video data, wherein the denoising process comprises: identifying vehicles, signal lights and traffic signs in the images and/or videos; obtaining a number of vehicles in the image and/or video; and extracting the state of the signal lamp and the meaning of the traffic sign;
analyzing the denoised image and/or video data to obtain road condition information of a road where the vehicle is located based on vehicle information data of the vehicle; and
and sending the road condition information.
2. The method for acquiring traffic information according to claim 1, wherein said vehicle information data includes data of coordinates of said vehicle.
3. The method for acquiring traffic information according to claim 2, wherein said vehicle information data further comprises data of speed and/or direction of said vehicle.
4. The method of claim 1, wherein identifying the vehicles, signal lights and traffic signs in the image and/or video comprises:
and identifying vehicles, signal lamps and traffic signs in the images and/or videos by extracting the histogram of direction gradient and the haar characteristics of the images and/or videos.
5. The method of claim 1, wherein obtaining the number of vehicles in the image and/or video comprises:
and obtaining the number of vehicles in the image and/or the video through spatial matrix operation.
6. The method of claim 1, wherein extracting the signal light status and the traffic sign comprises:
and extracting the state of the signal lamp and the meaning of the traffic sign through a pattern recognition algorithm.
7. The method as claimed in claim 1, wherein analyzing the de-noised image and/or video data to obtain the traffic information of the road on which the vehicle is located includes:
determining the vehicle density of the road on which the vehicle is positioned according to the number of vehicles counted from the image and/or video data in unit time and unit road area.
8. The method of claim 7, wherein the average vehicle density of the road is determined according to the vehicle density of the road obtained from each vehicle.
9. The method as claimed in claim 1, wherein analyzing the de-noised image and/or video data to obtain the traffic information of the road on which the vehicle is located includes:
and determining the average passing speed of the road on which the vehicle is positioned according to the vehicle information data of each vehicle.
10. The method as claimed in claim 1, wherein analyzing the de-noised image and/or video data to obtain the traffic information of the road on which the vehicle is located includes:
and determining the actual passing speed of the road where the vehicle is located according to the red light duration, the green light duration and the vehicle information data of the signal lamp.
11. The method according to claim 10, wherein the average traffic speed of the road on which the vehicle is located is determined according to the actual traffic speed of each vehicle.
12. The method of claim 1, further comprising:
determining the congestion level of each road according to the road condition information of each road; and
and generating navigation guide information based on the congestion level of each road.
13. A system for acquiring traffic information, comprising:
a vehicle information acquisition unit configured to acquire vehicle information data of a vehicle;
the image video acquisition unit is arranged on the vehicle and is configured to acquire images and/or video data of a road where the vehicle is located;
a data denoising processing unit configured to denoise the image and/or video data, the denoising processing including: identifying vehicles, signal lights and traffic signs in the images and/or videos; obtaining a number of vehicles in the image and/or video; and extracting the state of the signal lamp and the meaning of the traffic sign;
a data analysis unit configured to analyze the denoised image and/or video data to acquire road condition information of a road where the vehicle is located based on vehicle information data of the vehicle; and
an information transmitting unit configured to transmit the traffic information.
14. The system for acquiring road condition information according to claim 13, wherein the vehicle information data includes data of coordinates of the vehicle.
15. The system for acquiring traffic information according to claim 14, wherein said vehicle information data further comprises data of speed and/or direction of said vehicle.
16. The system for acquiring traffic information according to claim 13, wherein identifying vehicles, signal lights and traffic signs in said images and/or videos comprises:
and identifying vehicles, signal lamps and traffic signs in the images and/or videos by extracting the histogram of direction gradient and the haar characteristics of the images and/or videos.
17. The system for acquiring traffic information according to claim 13, wherein obtaining the number of vehicles in the image and/or video comprises:
and obtaining the number of vehicles in the image and/or the video through spatial matrix operation.
18. The system for acquiring traffic information according to claim 13, wherein the extracting the signal light status and the traffic sign meaning comprises:
and extracting the state of the signal lamp and the meaning of the traffic sign through a pattern recognition algorithm.
19. The system for acquiring traffic information according to claim 13, wherein analyzing the de-noised image and/or video data to acquire the traffic information of the road on which the vehicle is located comprises:
determining the vehicle density of the road on which the vehicle is positioned according to the number of vehicles counted from the image and/or video data in unit time and unit road area.
20. The system for obtaining road condition information according to claim 19, wherein the average vehicle density of the road is determined according to the vehicle density of the road obtained from each vehicle.
21. The system for acquiring traffic information according to claim 13, wherein analyzing the de-noised image and/or video data to acquire the traffic information of the road on which the vehicle is located comprises:
and determining the average passing speed of the road on which the vehicle is positioned according to the vehicle information data of each vehicle.
22. The system for acquiring traffic information according to claim 13, wherein analyzing the de-noised image and/or video data to acquire the traffic information of the road on which the vehicle is located comprises:
and determining the actual passing speed of the road where the vehicle is located according to the red light duration, the green light duration and the vehicle information data of the signal lamp.
23. The system for acquiring road condition information according to claim 22, wherein the average traffic speed of the road on which the vehicle is located is determined according to the actual traffic speed of each vehicle.
24. The system for acquiring traffic information according to claim 13, wherein the data analysis unit is further configured to:
determining the congestion level of each road according to the road condition information of each road; and
and generating navigation guide information based on the congestion level of each road.
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