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# Motion Analysis and Object Tracking

## calcOpticalFlowPyrLK

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

C++:`` void `calcOpticalFlowPyrLK`(InputArray prevImg, InputArray nextImg, InputArray prevPts, InputOutputArray nextPts, OutputArray status, OutputArray err, Size winSize=Size(21,21), int maxLevel=3, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags=0, double minEigThreshold=1e-4 )

Python:`` `cv2.``calcOpticalFlowPyrLK`(prevImg, nextImg, prevPts, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, flags[, minEigThreshold]]]]]]]) → nextPts, status, err

C:`` void `cvCalcOpticalFlowPyrLK`(const CvArr* prev, const CvArr* curr, CvArr* prev_pyr, CvArr* curr_pyr, const CvPoint2D32f* prev_features, CvPoint2D32f* curr_features, int count, CvSize win_size, int level, char* status, float* track_error, CvTermCriteria criteria, int flags)

  Parameters: | prevImg – first 8-bit input image or pyramid constructed by `buildOpticalFlowPyramid()`. nextImg – second input image or pyramid of the same size and the same type as `prevImg`. prevPts – vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers. nextPts – output vector of 2D points (with single-precision floating-point coordinates) containing the calculated new positions of input features in the second image; when `OPTFLOW_USE_INITIAL_FLOW` flag is passed, the vector must have the same size as in the input. status – output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. err – output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in `flags` parameter; if the flow wasn’t found then the error is not defined (use the `status` parameter to find such cases). winSize – size of the search window at each pyramid level. maxLevel – 0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than `maxLevel`. criteria – parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations `criteria.maxCount` or when the search window moves by less than `criteria.epsilon`. flags – operation flags: OPTFLOW_USE_INITIAL_FLOW uses initial estimations, stored in `nextPts`; if the flag is not set, then `prevPts` is copied to `nextPts` and is considered the initial estimate. OPTFLOW_LK_GET_MIN_EIGENVALS use minimum eigen values as an error measure (see `minEigThreshold` description); if the flag is not set, then L1 distance between patches around the original and a moved point, divided by number of pixels in a window, is used as a error measure. minEigThreshold – the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than `minEigThreshold`, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.

Note

- An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
- (Python) An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/python2/lk_track.py
- (Python) An example using the Lucas-Kanade tracker for homography matching can be found at opencv_source_code/samples/python2/lk_homography.py

## buildOpticalFlowPyramid

Constructs the image pyramid which can be passed to `calcOpticalFlowPyrLK()`.

C++:`` int `buildOpticalFlowPyramid`(InputArray img, OutputArrayOfArrays pyramid, Size winSize, int maxLevel, bool withDerivatives=true, int pyrBorder=BORDER_REFLECT_101, int derivBorder=BORDER_CONSTANT, bool tryReuseInputImage=true)

Python:`` `cv2.``buildOpticalFlowPyramid`(img, winSize, maxLevel[, pyramid[, withDerivatives[, pyrBorder[, derivBorder[, tryReuseInputImage]]]]]) → retval, pyramid

  Parameters: | img – 8-bit input image. pyramid – output pyramid. winSize – window size of optical flow algorithm. Must be not less than `winSize` argument of `calcOpticalFlowPyrLK()`. It is needed to calculate required padding for pyramid levels. maxLevel – 0-based maximal pyramid level number. withDerivatives – set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then `calcOpticalFlowPyrLK()` will calculate them internally. pyrBorder – the border mode for pyramid layers. derivBorder – the border mode for gradients. tryReuseInputImage – put ROI of input image into the pyramid if possible. You can pass `false` to force data copying.
  Returns: | number of levels in constructed pyramid. Can be less than `maxLevel`.

## calcOpticalFlowFarneback

Computes a dense optical flow using the Gunnar Farneback’s algorithm.

C++:`` void `calcOpticalFlowFarneback`(InputArray prev, InputArray next, InputOutputArray flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)

C:`` void `cvCalcOpticalFlowFarneback`(const CvArr* prev, const CvArr* next, CvArr* flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)

Python:`` `cv2.``calcOpticalFlowFarneback`(prev, next, flow, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags) → flow

  Parameters: | prev – first 8-bit single-channel input image. next – second input image of the same size and the same type as `prev`. flow – computed flow image that has the same size as `prev` and type `CV_32FC2`. pyr_scale – parameter, specifying the image scale (<1) to build pyramids for each image; `pyr_scale=0.5` means a classical pyramid, where each next layer is twice smaller than the previous one. levels – number of pyramid layers including the initial image; `levels=1` means that no extra layers are created and only the original images are used. winsize – averaging window size; larger values increase the algorithm robustness to image noise and give more chances for fast motion detection, but yield more blurred motion field. iterations – number of iterations the algorithm does at each pyramid level. poly_n – size of the pixel neighborhood used to find polynomial expansion in each pixel; larger values mean that the image will be approximated with smoother surfaces, yielding more robust algorithm and more blurred motion field, typically `poly_n` =5 or 7. poly_sigma – standard deviation of the Gaussian that is used to smooth derivatives used as a basis for the polynomial expansion; for `poly_n=5`, you can set `poly_sigma=1.1`, for `poly_n=7`, a good value would be `poly_sigma=1.5`. flags – operation flags that can be a combination of the following: OPTFLOW_USE_INITIAL_FLOW uses the input `flow` as an initial flow approximation. OPTFLOW_FARNEBACK_GAUSSIAN uses the Gaussian filter instead of a box filter of the same size for optical flow estimation; usually, this option gives z more accurate flow than with a box filter, at the cost of lower speed; normally, `winsize` for a Gaussian window should be set to a larger value to achieve the same level of robustness.

The function finds an optical flow for each `prev` pixel using the [Farneback2003] algorithm so that

Note

- An example using the optical flow algorithm described by Gunnar Farneback can be found at opencv_source_code/samples/cpp/fback.cpp
- (Python) An example using the optical flow algorithm described by Gunnar Farneback can be found at opencv_source_code/samples/python2/opt_flow.py

## estimateRigidTransform

Computes an optimal affine transformation between two 2D point sets.

C++:`` Mat `estimateRigidTransform`(InputArray src, InputArray dst, bool fullAffine)

Python:`` `cv2.``estimateRigidTransform`(src, dst, fullAffine) → retval

  Parameters: | src – First input 2D point set stored in `std::vector` or `Mat`, or an image stored in `Mat`. dst – Second input 2D point set of the same size and the same type as `A`, or another image. fullAffine – If true, the function finds an optimal affine transformation with no additional restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is limited to combinations of translation, rotation, and uniform scaling (5 degrees of freedom).

The function finds an optimal affine transform [A|b] (a `2 x 3` floating-point matrix) that approximates best the affine transformation between:

> - Two point sets
> - Two raster images. In this case, the function first finds some features in the `src` image and finds the corresponding features in `dst` image. After that, the problem is reduced to the first case.

In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix A and 2x1 vector b so that:

> where `src[i]` and `dst[i]` are the i-th points in `src` and `dst`, respectively

> can be either arbitrary (when `fullAffine=true` ) or have a form of

> when `fullAffine=false` .

See also

`getAffineTransform()`, `getPerspectiveTransform()`, `findHomography()`

## findTransformECC

Finds the geometric transform (warp) between two images in terms of the ECC criterion [EP08].

C++:`` double `findTransformECC`(InputArray templateImage, InputArray inputImage, InputOutputArray warpMatrix, int motionType=MOTION_AFFINE, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001))

Python:`` `cv2.``findTransformECC`(templateImage, inputImage, warpMatrix[, motionType[, criteria]]) → retval, warpMatrix

  Parameters: | templateImage – single-channel template image; `CV_8U` or `CV_32F` array. inputImage – single-channel input image which should be warped with the final `warpMatrix` in order to provide an image similar to `templateImage`, same type as `temlateImage`. warpMatrix – floating-point or mapping matrix (warp). motionType – parameter, specifying the type of motion: MOTION_TRANSLATION sets a translational motion model; `warpMatrix` is with the first part being the unity matrix and the rest two parameters being estimated. MOTION_EUCLIDEAN sets a Euclidean (rigid) transformation as motion model; three parameters are estimated; `warpMatrix` is . MOTION_AFFINE sets an affine motion model (DEFAULT); six parameters are estimated; `warpMatrix` is . MOTION_HOMOGRAPHY sets a homography as a motion model; eight parameters are estimated;``warpMatrix`` is . criteria – parameter, specifying the termination criteria of the ECC algorithm; `criteria.epsilon` defines the threshold of the increment in the correlation coefficient between two iterations (a negative `criteria.epsilon` makes `criteria.maxcount` the only termination criterion). Default values are shown in the declaration above.

The function estimates the optimum transformation (`warpMatrix`) with respect to ECC criterion ([EP08]), that is

where

(the equation holds with homogeneous coordinates for homography). It returns the final enhanced correlation coefficient, that is the correlation coefficient between the template image and the final warped input image. When a matrix is given with `motionType` =0, 1 or 2, the third row is ignored.

Unlike `findHomography()` and `estimateRigidTransform()`, the function `findTransformECC()` implements an area-based alignment that builds on intensity similarities. In essence, the function updates the initial transformation that roughly aligns the images. If this information is missing, the identity warp (unity matrix) should be given as input. Note that if images undergo strong displacements/rotations, an initial transformation that roughly aligns the images is necessary (e.g., a simple euclidean/similarity transform that allows for the images showing the same image content approximately). Use inverse warping in the second image to take an image close to the first one, i.e. use the flag `WARP_INVERSE_MAP` with `warpAffine()` or `warpPerspective()`. See also the OpenCV sample `image_alignment.cpp` that demonstrates the use of the function. Note that the function throws an exception if algorithm does not converges.

See also

`estimateRigidTransform()`, `findHomography()`

## CamShift

Finds an object center, size, and orientation.

C++:`` RotatedRect `CamShift`(InputArray probImage, Rect& window, TermCriteria criteria)

Python:`` `cv2.``CamShift`(probImage, window, criteria) → retval, window

C:`` int `cvCamShift`(const CvArr* prob_image, CvRect window, CvTermCriteria criteria, CvConnectedComp* comp, CvBox2D* box=NULL )

  Parameters: | probImage – Back projection of the object histogram. See `calcBackProject()` . window – Initial search window. criteria – Stop criteria for the underlying `meanShift()` .
  Returns: | (in old interfaces) Number of iterations CAMSHIFT took to converge

The function implements the CAMSHIFT object tracking algorithm [Bradski98]. First, it finds an object center using `meanShift()` and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with `RotatedRect::boundingRect()` .

See the OpenCV sample `camshiftdemo.c` that tracks colored objects.

Note

- (Python) A sample explaining the camshift tracking algorithm can be found at opencv_source_code/samples/python2/camshift.py

## meanShift

Finds an object on a back projection image.

C++:`` int `meanShift`(InputArray probImage, Rect& window, TermCriteria criteria)

Python:`` `cv2.``meanShift`(probImage, window, criteria) → retval, window

C:`` int `cvMeanShift`(const CvArr* prob_image, CvRect window, CvTermCriteria criteria, CvConnectedComp* comp)

  Parameters: | probImage – Back projection of the object histogram. See `calcBackProject()` for details. window – Initial search window. criteria – Stop criteria for the iterative search algorithm.
  Returns: | Number of iterations CAMSHIFT took to converge.

The function implements the iterative object search algorithm. It takes the input back projection of an object and the initial position. The mass center in `window` of the back projection image is computed and the search window center shifts to the mass center. The procedure is repeated until the specified number of iterations `criteria.maxCount` is done or until the window center shifts by less than `criteria.epsilon` . The algorithm is used inside `CamShift()` and, unlike `CamShift()` , the search window size or orientation do not change during the search. You can simply pass the output of `calcBackProject()` to this function. But better results can be obtained if you pre-filter the back projection and remove the noise. For example, you can do this by retrieving connected components with `findContours()` , throwing away contours with small area ( `contourArea()` ), and rendering the remaining contours with `drawContours()` .

Note

- A mean-shift tracking sample can be found at opencv_source_code/samples/cpp/camshiftdemo.cpp

## KalmanFilter

class `KalmanFilter`

  Kalman filter class.

The class implements a standard Kalman filter http://en.wikipedia.org/wiki/Kalman_filter, [Welch95]. However, you can modify `transitionMatrix`, `controlMatrix`, and `measurementMatrix` to get an extended Kalman filter functionality. See the OpenCV sample `kalman.cpp` .

Note

- An example using the standard Kalman filter can be found at opencv_source_code/samples/cpp/kalman.cpp

## KalmanFilter::KalmanFilter

The constructors.

C++:`` `KalmanFilter::``KalmanFilter`()

C++:`` `KalmanFilter::``KalmanFilter`(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)

Python:`` `cv2.``KalmanFilter`([dynamParams, measureParams[, controlParams[, type]]]) → <KalmanFilter object>

C:`` CvKalman* `cvCreateKalman`(int dynam_params, int measure_params, int control_params=0 )

  The full constructor.

  Parameters: | dynamParams – Dimensionality of the state. measureParams – Dimensionality of the measurement. controlParams – Dimensionality of the control vector. type – Type of the created matrices that should be `CV_32F` or `CV_64F`.

Note

In C API when `CvKalman* kalmanFilter` structure is not needed anymore, it should be released with `cvReleaseKalman(&kalmanFilter)`

## KalmanFilter::init

Re-initializes Kalman filter. The previous content is destroyed.

C++:`` void `KalmanFilter::``init`(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)

  Parameters: | dynamParams – Dimensionalityensionality of the state. measureParams – Dimensionality of the measurement. controlParams – Dimensionality of the control vector. type – Type of the created matrices that should be `CV_32F` or `CV_64F`.

## KalmanFilter::predict

Computes a predicted state.

C++:`` const Mat& `KalmanFilter::``predict`(const Mat& control=Mat())

Python:`` `cv2.KalmanFilter.``predict`([control]) → retval

C:`` const CvMat* `cvKalmanPredict`(CvKalman* kalman, const CvMat* control=NULL)

  Parameters: | control – The optional input control

## KalmanFilter::correct

Updates the predicted state from the measurement.

C++:`` const Mat& `KalmanFilter::``correct`(const Mat& measurement)

Python:`` `cv2.KalmanFilter.``correct`(measurement) → retval

C:`` const CvMat* `cvKalmanCorrect`(CvKalman* kalman, const CvMat* measurement)

  Parameters: | measurement – The measured system parameters

## BackgroundSubtractor

class `BackgroundSubtractor` : public `Algorithm`

Base class for background/foreground segmentation.

    class BackgroundSubtractor : public Algorithm
    {
    public:
        virtual ~BackgroundSubtractor();
        virtual void apply(InputArray image, OutputArray fgmask, double learningRate=0);
        virtual void getBackgroundImage(OutputArray backgroundImage) const;
    };

The class is only used to define the common interface for the whole family of background/foreground segmentation algorithms.

## BackgroundSubtractor::apply

Computes a foreground mask.

C++:`` void `BackgroundSubtractor::``apply`(InputArray image, OutputArray fgmask, double learningRate=-1)

Python:`` `cv2.BackgroundSubtractor.``apply`(image[, fgmask[, learningRate]]) → fgmask

  Parameters: | image – Next video frame. fgmask – The output foreground mask as an 8-bit binary image. learningRate – The value between 0 and 1 that indicates how fast the background model is learnt. Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means that the background model is not updated at all, 1 means that the background model is completely reinitialized from the last frame.

## BackgroundSubtractor::getBackgroundImage

Computes a background image.

C++:`` void `BackgroundSubtractor::``getBackgroundImage`(OutputArray backgroundImage)`const`

  Parameters: | backgroundImage – The output background image.

Note

Sometimes the background image can be very blurry, as it contain the average background statistics.

## BackgroundSubtractorMOG

class `BackgroundSubtractorMOG` : public `BackgroundSubtractor`

Gaussian Mixture-based Background/Foreground Segmentation Algorithm.

The class implements the algorithm described in [KB2001].

## createBackgroundSubtractorMOG

Creates mixture-of-gaussian background subtractor

C++:`` Ptr<BackgroundSubtractorMOG> `createBackgroundSubtractorMOG`(int history=200, int nmixtures=5, double backgroundRatio=0.7, double noiseSigma=0)

Python:`` `cv2.``createBackgroundSubtractorMOG`([history[, nmixtures[, backgroundRatio[, noiseSigma]]]]) → retval

  Parameters: | history – Length of the history. nmixtures – Number of Gaussian mixtures. backgroundRatio – Background ratio. noiseSigma – Noise strength (standard deviation of the brightness or each color channel). 0 means some automatic value.

## BackgroundSubtractorMOG2

Gaussian Mixture-based Background/Foreground Segmentation Algorithm.

class `BackgroundSubtractorMOG2` : public `BackgroundSubtractor`

The class implements the Gaussian mixture model background subtraction described in [Zivkovic2004] and [Zivkovic2006] .

## createBackgroundSubtractorMOG2

Creates MOG2 Background Subtractor

C++:`` Ptr<BackgroundSubtractorMOG2> `createBackgroundSubtractorMOG2`(int history=500, double varThreshold=16, bool detectShadows=true )

  Parameters: | history – Length of the history. varThreshold – Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update. detectShadows – If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

## BackgroundSubtractorMOG2::getHistory

Returns the number of last frames that affect the background model

C++:`` int `BackgroundSubtractorMOG2::``getHistory`()`const`

## BackgroundSubtractorMOG2::setHistory

Sets the number of last frames that affect the background model

C++:`` void `BackgroundSubtractorMOG2::``setHistory`(int history)

## BackgroundSubtractorMOG2::getNMixtures

Returns the number of gaussian components in the background model

C++:`` int `BackgroundSubtractorMOG2::``getNMixtures`()`const`

## BackgroundSubtractorMOG2::setNMixtures

Sets the number of gaussian components in the background model. The model needs to be reinitalized to reserve memory.

C++:`` void `BackgroundSubtractorMOG2::``setNMixtures`(int nmixtures)

## BackgroundSubtractorMOG2::getBackgroundRatio

Returns the “background ratio” parameter of the algorithm

C++:`` double `BackgroundSubtractorMOG2::``getBackgroundRatio`()`const`

If a foreground pixel keeps semi-constant value for about `backgroundRatio*history` frames, it’s considered background and added to the model as a center of a new component. It corresponds to `TB` parameter in the paper.

## BackgroundSubtractorMOG2::setBackgroundRatio

Sets the “background ratio” parameter of the algorithm

C++:`` void `BackgroundSubtractorMOG2::``setBackgroundRatio`(double ratio)

## BackgroundSubtractorMOG2::getVarThreshold

Returns the variance threshold for the pixel-model match

C++:`` double `BackgroundSubtractorMOG2::``getVarThreshold`()`const`

The main threshold on the squared Mahalanobis distance to decide if the sample is well described by the background model or not. Related to Cthr from the paper.

## BackgroundSubtractorMOG2::setVarThreshold

Sets the variance threshold for the pixel-model match

C++:`` void `BackgroundSubtractorMOG2::``setVarThreshold`(double varThreshold)

## BackgroundSubtractorMOG2::getVarThresholdGen

Returns the variance threshold for the pixel-model match used for new mixture component generation

C++:`` double `BackgroundSubtractorMOG2::``getVarThresholdGen`()`const`

Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the existing components (corresponds to `Tg` in the paper). If a pixel is not close to any component, it is considered foreground or added as a new component. `3 sigma => Tg=3*3=9` is default. A smaller `Tg` value generates more components. A higher `Tg` value may result in a small number of components but they can grow too large.

## BackgroundSubtractorMOG2::setVarThresholdGen

Sets the variance threshold for the pixel-model match used for new mixture component generation

C++:`` void `BackgroundSubtractorMOG2::``setVarThresholdGen`(double varThresholdGen)

## BackgroundSubtractorMOG2::getVarInit

Returns the initial variance of each gaussian component

C++:`` double `BackgroundSubtractorMOG2::``getVarInit`()`const`

## BackgroundSubtractorMOG2::setVarInit

Sets the initial variance of each gaussian component

C++:`` void `BackgroundSubtractorMOG2::``setVarInit`(double varInit)

## BackgroundSubtractorMOG2::getComplexityReductionThreshold

Returns the complexity reduction threshold

C++:`` double `BackgroundSubtractorMOG2::``getComplexityReductionThreshold`()`const`

This parameter defines the number of samples needed to accept to prove the component exists. `CT=0.05` is a default value for all the samples. By setting `CT=0` you get an algorithm very similar to the standard Stauffer&Grimson algorithm.

## BackgroundSubtractorMOG2::setComplexityReductionThreshold

Sets the complexity reduction threshold

C++:`` void `BackgroundSubtractorMOG2::``setComplexityReductionThreshold`(double ct)

## BackgroundSubtractorMOG2::getDetectShadows

Returns the shadow detection flag

C++:`` bool `BackgroundSubtractorMOG2::``getDetectShadows`()`const`

If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for details.

## BackgroundSubtractorMOG2::setDetectShadows

Enables or disables shadow detection

C++:`` void `BackgroundSubtractorMOG2::``setDetectShadows`(bool detectShadows)

## BackgroundSubtractorMOG2::getShadowValue

Returns the shadow value

C++:`` int `BackgroundSubtractorMOG2::``getShadowValue`()`const`

Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 in the mask always means background, 255 means foreground.

## BackgroundSubtractorMOG2::setShadowValue

Sets the shadow value

C++:`` void `BackgroundSubtractorMOG2::``setShadowValue`(int value)

## BackgroundSubtractorMOG2::getShadowThreshold

Returns the shadow threshold

C++:`` double `BackgroundSubtractorMOG2::``getShadowThreshold`()`const`

A shadow is detected if pixel is a darker version of the background. The shadow threshold (`Tau` in the paper) is a threshold defining how much darker the shadow can be. `Tau= 0.5` means that if a pixel is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiarra, Detecting Moving Shadows..., IEEE PAMI,2003.

## BackgroundSubtractorMOG2::setShadowThreshold

Sets the shadow threshold

C++:`` void `BackgroundSubtractorMOG2::``setShadowThreshold`(double threshold)

## BackgroundSubtractorKNN

K-nearest neigbours - based Background/Foreground Segmentation Algorithm.

class `BackgroundSubtractorKNN` : public `BackgroundSubtractor`

The class implements the K-nearest neigbours background subtraction described in [Zivkovic2006] . Very efficient if number of foreground pixels is low.

## createBackgroundSubtractorKNN

Creates KNN Background Subtractor

C++:`` Ptr<BackgroundSubtractorKNN> `createBackgroundSubtractorKNN`(int history=500, double dist2Threshold=400.0, bool detectShadows=true )

  Parameters: | history – Length of the history. dist2Threshold – Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update. detectShadows – If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

## BackgroundSubtractorKNN::getHistory

Returns the number of last frames that affect the background model

C++:`` int `BackgroundSubtractorKNN::``getHistory`()`const`

## BackgroundSubtractorKNN::setHistory

Sets the number of last frames that affect the background model

C++:`` void `BackgroundSubtractorKNN::``setHistory`(int history)

## BackgroundSubtractorKNN::getNSamples

Returns the number of data samples in the background model

C++:`` int `BackgroundSubtractorKNN::``getNSamples`()`const`

## BackgroundSubtractorKNN::setNSamples

Sets the number of data samples in the background model. The model needs to be reinitalized to reserve memory.

C++:`` void `BackgroundSubtractorKNN::``setNSamples`(int _nN)

## BackgroundSubtractorKNN::getDist2Threshold

Returns the threshold on the squared distance between the pixel and the sample

C++:`` double `BackgroundSubtractorKNN::``getDist2Threshold`()`const`

The threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to a data sample.

## BackgroundSubtractorKNN::setDist2Threshold

Sets the threshold on the squared distance

C++:`` void `BackgroundSubtractorKNN::``setDist2Threshold`(double _dist2Threshold)

## BackgroundSubtractorKNN::getkNNSamples

Returns the number of neighbours, the k in the kNN. K is the number of samples that need to be within dist2Threshold in order to decide that that pixel is matching the kNN background model.

C++:`` int `BackgroundSubtractorKNN::``getkNNSamples`()`const`

## BackgroundSubtractorKNN::setkNNSamples

Sets the k in the kNN. How many nearest neigbours need to match.

C++:`` void `BackgroundSubtractorKNN::``setkNNSamples`(int _nkNN)

## BackgroundSubtractorKNN::getDetectShadows

Returns the shadow detection flag

C++:`` bool `BackgroundSubtractorKNN::``getDetectShadows`()`const`

If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for details.

## BackgroundSubtractorKNN::setDetectShadows

Enables or disables shadow detection

C++:`` void `BackgroundSubtractorKNN::``setDetectShadows`(bool detectShadows)

## BackgroundSubtractorKNN::getShadowValue

Returns the shadow value

C++:`` int `BackgroundSubtractorKNN::``getShadowValue`()`const`

Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 in the mask always means background, 255 means foreground.

## BackgroundSubtractorKNN::setShadowValue

Sets the shadow value

C++:`` void `BackgroundSubtractorKNN::``setShadowValue`(int value)

## BackgroundSubtractorKNN::getShadowThreshold

Returns the shadow threshold

C++:`` double `BackgroundSubtractorKNN::``getShadowThreshold`()`const`

A shadow is detected if pixel is a darker version of the background. The shadow threshold (`Tau` in the paper) is a threshold defining how much darker the shadow can be. `Tau= 0.5` means that if a pixel is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiarra, Detecting Moving Shadows..., IEEE PAMI,2003.

## BackgroundSubtractorKNN::setShadowThreshold

Sets the shadow threshold

C++:`` void `BackgroundSubtractorKNN::``setShadowThreshold`(double threshold)

## BackgroundSubtractorGMG

Background Subtractor module based on the algorithm given in [Gold2012].

class `BackgroundSubtractorGMG` : public `BackgroundSubtractor`

## createBackgroundSubtractorGMG

Creates a GMG Background Subtractor

C++:`` Ptr<BackgroundSubtractorGMG> `createBackgroundSubtractorGMG`(int initializationFrames=120, double decisionThreshold=0.8)

Python:`` `cv2.``createBackgroundSubtractorGMG`([initializationFrames[, decisionThreshold]]) → retval

  Parameters: | initializationFrames – number of frames used to initialize the background models. decisionThreshold – Threshold value, above which it is marked foreground, else background.

## BackgroundSubtractorGMG::getNumFrames

Returns the number of frames used to initialize background model.

C++:`` int `BackgroundSubtractorGMG::``getNumFrames`()`const`

## BackgroundSubtractorGMG::setNumFrames

Sets the number of frames used to initialize background model.

C++:`` void `BackgroundSubtractorGMG::``setNumFrames`(int nframes)

## BackgroundSubtractorGMG::getDefaultLearningRate

Returns the learning rate of the algorithm. It lies between 0.0 and 1.0. It determines how quickly features are “forgotten” from histograms.

C++:`` double `BackgroundSubtractorGMG::``getDefaultLearningRate`()`const`

## BackgroundSubtractorGMG::setDefaultLearningRate

Sets the learning rate of the algorithm.

C++:`` void `BackgroundSubtractorGMG::``setDefaultLearningRate`(double lr)

## BackgroundSubtractorGMG::getDecisionThreshold

Returns the value of decision threshold. Decision value is the value above which pixel is determined to be FG.

C++:`` double `BackgroundSubtractorGMG::``getDecisionThreshold`()`const`

## BackgroundSubtractorGMG::setDecisionThreshold

Sets the value of decision threshold.

C++:`` void `BackgroundSubtractorGMG::``setDecisionThreshold`(double thresh)

## BackgroundSubtractorGMG::getMaxFeatures

Returns total number of distinct colors to maintain in histogram.

C++:`` int `BackgroundSubtractorGMG::``getMaxFeatures`()`const`

## BackgroundSubtractorGMG::setMaxFeatures

Sets total number of distinct colors to maintain in histogram.

C++:`` void `BackgroundSubtractorGMG::``setMaxFeatures`(int maxFeatures)

## BackgroundSubtractorGMG::getQuantizationLevels

Returns the parameter used for quantization of color-space. It is the number of discrete levels in each channel to be used in histograms.

C++:`` int `BackgroundSubtractorGMG::``getQuantizationLevels`()`const`

## BackgroundSubtractorGMG::setQuantizationLevels

Sets the parameter used for quantization of color-space

C++:`` void `BackgroundSubtractorGMG::``setQuantizationLevels`(int nlevels)

## BackgroundSubtractorGMG::getSmoothingRadius

Returns the kernel radius used for morphological operations

C++:`` int `BackgroundSubtractorGMG::``getSmoothingRadius`()`const`

## BackgroundSubtractorGMG::setSmoothingRadius

Sets the kernel radius used for morphological operations

C++:`` void `BackgroundSubtractorGMG::``setSmoothingRadius`(int radius)

## BackgroundSubtractorGMG::getUpdateBackgroundModel

Returns the status of background model update

C++:`` bool `BackgroundSubtractorGMG::``getUpdateBackgroundModel`()`const`

## BackgroundSubtractorGMG::setUpdateBackgroundModel

Sets the status of background model update

C++:`` void `BackgroundSubtractorGMG::``setUpdateBackgroundModel`(bool update)

## BackgroundSubtractorGMG::getMinVal

Returns the minimum value taken on by pixels in image sequence. Usually 0.

C++:`` double `BackgroundSubtractorGMG::``getMinVal`()`const`

## BackgroundSubtractorGMG::setMinVal

Sets the minimum value taken on by pixels in image sequence.

C++:`` void `BackgroundSubtractorGMG::``setMinVal`(double val)

## BackgroundSubtractorGMG::getMaxVal

Returns the maximum value taken on by pixels in image sequence. e.g. 1.0 or 255.

C++:`` double `BackgroundSubtractorGMG::``getMaxVal`()`const`

## BackgroundSubtractorGMG::setMaxVal

Sets the maximum value taken on by pixels in image sequence.

C++:`` void `BackgroundSubtractorGMG::``setMaxVal`(double val)

## BackgroundSubtractorGMG::getBackgroundPrior

Returns the prior probability that each individual pixel is a background pixel.

C++:`` double `BackgroundSubtractorGMG::``getBackgroundPrior`()`const`

## BackgroundSubtractorGMG::setBackgroundPrior

Sets the prior probability that each individual pixel is a background pixel.

C++:`` void `BackgroundSubtractorGMG::``setBackgroundPrior`(double bgprior)

## createOptFlow_DualTVL1

“Dual TV L1” Optical Flow Algorithm.

C++:`` Ptr<DenseOpticalFlow> `createOptFlow_DualTVL1`()

  The class implements the “Dual TV L1” optical flow algorithm described in [Zach2007] and [Javier2012] .

  Here are important members of the class that control the algorithm, which you can set after constructing the class instance:

  > double `tau`

  >   Time step of the numerical scheme.

  > double `lambda`

  >   Weight parameter for the data term, attachment parameter. This is the most relevant parameter, which determines the smoothness of the output. The smaller this parameter is, the smoother the solutions we obtain. It depends on the range of motions of the images, so its value should be adapted to each image sequence.

  > double `theta`

  >   Weight parameter for (u - v)^2, tightness parameter. It serves as a link between the attachment and the regularization terms. In theory, it should have a small value in order to maintain both parts in correspondence. The method is stable for a large range of values of this parameter.

  > int `nscales`

  >   Number of scales used to create the pyramid of images.

  > int `warps`

  >   Number of warpings per scale. Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the method. It also affects the running time, so it is a compromise between speed and accuracy.

  > double `epsilon`

  >   Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time. A small value will yield more accurate solutions at the expense of a slower convergence.

  > int `iterations`

  >   Stopping criterion iterations number used in the numerical scheme.

## DenseOpticalFlow::calc

Calculates an optical flow.

C++:`` void `DenseOpticalFlow::``calc`(InputArray I0, InputArray I1, InputOutputArray flow)

  Parameters: | prev – first 8-bit single-channel input image. next – second input image of the same size and the same type as `prev` . flow – computed flow image that has the same size as `prev` and type `CV_32FC2` .

## DenseOpticalFlow::collectGarbage

Releases all inner buffers.

C++:`` void `DenseOpticalFlow::``collectGarbage`()

[Bouguet00] | (1, 2) Jean-Yves Bouguet. Pyramidal Implementation of the Lucas Kanade Feature Tracker.

[Bradski98] | Bradski, G.R. “Computer Vision Face Tracking for Use in a Perceptual User Interface”, Intel, 1998

[EP08] | (1, 2) Evangelidis, G.D. and Psarakis E.Z. “Parametric Image Alignment using Enhanced Correlation Coefficient Maximization”, IEEE Transactions on PAMI, vol. 32, no. 10, 2008

[Farneback2003] | Gunnar Farneback, Two-frame motion estimation based on polynomial expansion, Lecture Notes in Computer Science, 2003, (2749), , 363-370.

[Horn81] | Berthold K.P. Horn and Brian G. Schunck. Determining Optical Flow. Artificial Intelligence, 17, pp. 185-203, 1981.

[KB2001] | KadewTraKuPong and R. Bowden. “An improved adaptive background mixture model for real-time tracking with shadow detection”, Proc. 2nd European Workshop on Advanced Video-Based Surveillance Systems, 2001: http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf

[Javier2012] | Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. “TV-L1 Optical Flow Estimation”.

[Lucas81] | Lucas, B., and Kanade, T. An Iterative Image Registration Technique with an Application to Stereo Vision, Proc. of 7th International Joint Conference on Artificial Intelligence (IJCAI), pp. 674-679.

[Welch95] | Greg Welch and Gary Bishop “An Introduction to the Kalman Filter”, 1995

[Zach2007] | Zach, T. Pock and H. Bischof. “A Duality Based Approach for Realtime TV-L1 Optical Flow”, In Proceedings of Pattern Recognition (DAGM), Heidelberg, Germany, pp. 214-223, 2007

[Zivkovic2004] | Zivkovic. “Improved adaptive Gausian mixture model for background subtraction”, International Conference Pattern Recognition, UK, August, 2004, http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf. The code is very fast and performs also shadow detection. Number of Gausssian components is adapted per pixel.

[Zivkovic2006] | (1, 2) Z.Zivkovic, F. van der Heijden. “Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction”, Pattern Recognition Letters, vol. 27, no. 7, pages 773-780, 2006.

[Gold2012] | Andrew B. Godbehere, Akihiro Matsukawa, Ken Goldberg, “Visual Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive Audio Art Installation”, American Control Conference, Montreal, June 2012.

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