Motion Analysis and Object Tracking
calcOpticalFlowPyrLK
Calculates an optical flow for a sparse feature set using the iterative LucasKanade 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=1e4 )

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)

Python: cv.CalcOpticalFlowPyrLK(prev, curr, prevPyr, currPyr, prevFeatures, winSize, level, criteria, flags, guesses=None) > (currFeatures, status, track_error)
Parameters: 
 prevImg – first 8bit 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 singleprecision floatingpoint numbers.
 nextPts – output vector of 2D points (with singleprecision floatingpoint 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 – 0based 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 LucasKanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.
Note
 An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
 (Python) An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/python2/lk_track.py
 (Python) An example using the LucasKanade 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 – 8bit 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 – 0based 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, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags[, flow]) → flow

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
updateMotionHistory
Updates the motion history image by a moving silhouette.

C++: void updateMotionHistory(InputArray silhouette, InputOutputArray mhi, double timestamp, double duration)

Python: cv2.updateMotionHistory(silhouette, mhi, timestamp, duration) → None

C: void cvUpdateMotionHistory(const CvArr* silhouette, CvArr* mhi, double timestamp, double duration)

Python: cv.UpdateMotionHistory(silhouette, mhi, timestamp, duration) → None
Parameters: 
 silhouette – Silhouette mask that has nonzero pixels where the motion occurs.
 mhi – Motion history image that is updated by the function (singlechannel, 32bit floatingpoint).
 timestamp – Current time in milliseconds or other units.
 duration – Maximal duration of the motion track in the same units as timestamp .

The function updates the motion history image as follows:
That is, MHI pixels where the motion occurs are set to the current timestamp , while the pixels where the motion happened last time a long time ago are cleared.
The function, together with
calcMotionGradient() and
calcGlobalOrientation() , implements a motion templates technique described in
[Davis97] and [Bradski00].
See also the OpenCV sample motempl.c that demonstrates the use of all the motion template functions.
calcMotionGradient
Calculates a gradient orientation of a motion history image.

C++: void calcMotionGradient(InputArray mhi, OutputArray mask, OutputArray orientation, double delta1, double delta2, int apertureSize=3 )

Python: cv2.calcMotionGradient(mhi, delta1, delta2[, mask[, orientation[, apertureSize]]]) → mask, orientation

C: void cvCalcMotionGradient(const CvArr* mhi, CvArr* mask, CvArr* orientation, double delta1, double delta2, int aperture_size=3 )

Python: cv.CalcMotionGradient(mhi, mask, orientation, delta1, delta2, apertureSize=3) → None

The function calculates a gradient orientation at each pixel
as:
In fact,
fastAtan2() and
phase() are used so that the computed angle is measured in degrees and covers the full range 0..360. Also, the mask is filled to indicate pixels where the computed angle is valid.
Note
 (Python) An example on how to perform a motion template technique can be found at opencv_source_code/samples/python2/motempl.py
calcGlobalOrientation
Calculates a global motion orientation in a selected region.

C++: double calcGlobalOrientation(InputArray orientation, InputArray mask, InputArray mhi, double timestamp, double duration)

Python: cv2.calcGlobalOrientation(orientation, mask, mhi, timestamp, duration) → retval

C: double cvCalcGlobalOrientation(const CvArr* orientation, const CvArr* mask, const CvArr* mhi, double timestamp, double duration)

Python: cv.CalcGlobalOrientation(orientation, mask, mhi, timestamp, duration) → float

The function calculates an average
motion direction in the selected region and returns the angle between
0 degrees and 360 degrees. The average direction is computed from
the weighted orientation histogram, where a recent motion has a larger
weight and the motion occurred in the past has a smaller weight, as recorded in mhi .
segmentMotion
Splits a motion history image into a few parts corresponding to separate independent motions (for example, left hand, right hand).

C++: void segmentMotion(InputArray mhi, OutputArray segmask, vector<Rect>& boundingRects, double timestamp, double segThresh)

Python: cv2.segmentMotion(mhi, timestamp, segThresh[, segmask]) → segmask, boundingRects

C: CvSeq* cvSegmentMotion(const CvArr* mhi, CvArr* seg_mask, CvMemStorage* storage, double timestamp, double seg_thresh)

Python: cv.SegmentMotion(mhi, seg_mask, storage, timestamp, seg_thresh) → boundingRects
Parameters: 
 mhi – Motion history image.
 segmask – Image where the found mask should be stored, singlechannel, 32bit floatingpoint.
 boundingRects – Vector containing ROIs of motion connected components.
 timestamp – Current time in milliseconds or other units.
 segThresh – Segmentation threshold that is recommended to be equal to the interval between motion history “steps” or greater.

The function finds all of the motion segments and marks them in segmask with individual values (1,2,...). It also computes a vector with ROIs of motion connected components. After that the motion direction for every component can be calculated with calcGlobalOrientation() using the extracted mask of the particular component.
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 )

Python: cv.CamShift(prob_image, window, criteria) > (int, comp, box)
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)

Python: cv.MeanShift(prob_image, window, criteria) → 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 prefilter 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 meanshift 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 )

Python: cv.CreateKalman(dynam_params, measure_params, control_params=0) → CvKalman
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
Reinitializes 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)

Python: cv.KalmanPredict(kalman, control=None) → mat
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)

Python: cv.KalmanCorrect(kalman, measurement) → mat
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 operator()(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::operator()
Computes a foreground mask.

C++: void BackgroundSubtractor::operator()(InputArray image, OutputArray fgmask, double learningRate=0)

Python: cv2.BackgroundSubtractor.apply(image[, fgmask[, learningRate]]) → fgmask
Parameters: 
 image – Next video frame.
 fgmask – The output foreground mask as an 8bit binary image.

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 Mixturebased Background/Foreground Segmentation Algorithm.
The class implements the algorithm described in P. KadewTraKuPong and R. Bowden, An improved adaptive background mixture model for realtime tracking with shadow detection, Proc. 2nd European Workshop on Advanced VideoBased Surveillance Systems, 2001: http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
BackgroundSubtractorMOG::BackgroundSubtractorMOG
The constructors.

C++: BackgroundSubtractorMOG::BackgroundSubtractorMOG()

C++: BackgroundSubtractorMOG::BackgroundSubtractorMOG(int history, int nmixtures, double backgroundRatio, double noiseSigma=0)

Python: cv2.BackgroundSubtractorMOG([history, nmixtures, backgroundRatio[, noiseSigma]]) → <BackgroundSubtractorMOG object>
Parameters: 
 history – Length of the history.
 nmixtures – Number of Gaussian mixtures.
 backgroundRatio – Background ratio.
 noiseSigma – Noise strength.

Default constructor sets all parameters to default values.
BackgroundSubtractorMOG::operator()
Updates the background model and returns the foreground mask

C++: void BackgroundSubtractorMOG::operator()(InputArray image, OutputArray fgmask, double learningRate=0)
Parameters are the same as in BackgroundSubtractor::operator()
BackgroundSubtractorMOG2
Gaussian Mixturebased Background/Foreground Segmentation Algorithm.

class BackgroundSubtractorMOG2 : public BackgroundSubtractor
Here are important members of the class that control the algorithm, which you can set after constructing the class instance:

int nmixtures
Maximum allowed number of mixture components. Actual number is determined dynamically per pixel.

float backgroundRatio
Threshold defining whether the component is significant enough to be included into the background model ( corresponds to TB=1cf from the paper??which paper??). cf=0.1 => TB=0.9 is default. For alpha=0.001, it means that the mode should exist for approximately 105 frames before it is considered foreground.

float varThresholdGen
Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the existing components (corresponds to Tg). If it is not close to any component, a new component is generated. 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.

float fVarInit
Initial variance for the newly generated components. It affects the speed of adaptation. The parameter value is based on your estimate of the typical standard deviation from the images. OpenCV uses 15 as a reasonable value.

float fVarMin
Parameter used to further control the variance.

float fVarMax
Parameter used to further control the variance.

float fCT
Complexity reduction parameter. 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.

uchar nShadowDetection
The value for marking shadow pixels in the output foreground mask. Default value is 127.

float fTau
Shadow threshold. The shadow is detected if the pixel is a darker version of the background. Tau 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,Cucchiarra, Detecting Moving Shadows..., IEEE PAMI,2003.
The class implements the Gaussian mixture model background subtraction described in:
 Z.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.
 Z.Zivkovic, F. van der Heijden, Efficient Adaptive Density Estimapion per Image Pixel for the Task of Background Subtraction, Pattern Recognition Letters, vol. 27, no. 7, pages 773780, 2006. The algorithm similar to the standard Stauffer&Grimson algorithm with additional selection of the number of the Gaussian components based on: Z.Zivkovic, F.van der Heijden, Recursive unsupervised learning of finite mixture models, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.26, no.5, pages 651656, 2004.
BackgroundSubtractorMOG2::BackgroundSubtractorMOG2
The constructors.

C++: BackgroundSubtractorMOG2::BackgroundSubtractorMOG2()

C++: BackgroundSubtractorMOG2::BackgroundSubtractorMOG2(int history, float varThreshold, bool bShadowDetection=true )
Parameters: 
 history – Length of the history.
 varThreshold – Threshold on the squared Mahalanobis distance to decide whether it is well described by the background model (see Cthr??). This parameter does not affect the background update. A typical value could be 4 sigma, that is, varThreshold=4*4=16; (see Tb??).
 bShadowDetection – Parameter defining whether shadow detection should be enabled (true or false).

BackgroundSubtractorMOG2::operator()
Updates the background model and computes the foreground mask

C++: void BackgroundSubtractorMOG2::operator()(InputArray image, OutputArray fgmask, double learningRate=1)
See BackgroundSubtractor::operator().
calcOpticalFlowSF
Calculate an optical flow using “SimpleFlow” algorithm.

C++: void calcOpticalFlowSF(Mat& from, Mat& to, Mat& flow, int layers, int averaging_block_size, int max_flow)

C++: void calcOpticalFlowSF(Mat& from, Mat& to, Mat& flow, int layers, int averaging_block_size, int max_flow, double sigma_dist, double sigma_color, int postprocess_window, double sigma_dist_fix, double sigma_color_fix, double occ_thr, int upscale_averaging_radius, double upscale_sigma_dist, double upscale_sigma_color, double speed_up_thr)
Parameters: 
 prev – First 8bit 3channel image.
 next – Second 8bit 3channel image
 flowX – Xcoordinate of estimated flow
 flowY – Ycoordinate of estimated flow
 layers – Number of layers
 averaging_block_size – Size of block through which we sum up when calculate cost function for pixel
 max_flow – maximal flow that we search at each level
 sigma_dist – vector smooth spatial sigma parameter
 sigma_color – vector smooth color sigma parameter
 postprocess_window – window size for postprocess cross bilateral filter
 sigma_dist_fix – spatial sigma for postprocess cross bilateralf filter
 sigma_color_fix – color sigma for postprocess cross bilateral filter
 occ_thr – threshold for detecting occlusions
 upscale_averaging_radiud – window size for bilateral upscale operation
 upscale_sigma_dist – spatial sigma for bilateral upscale operation
 upscale_sigma_color – color sigma for bilateral upscale operation
 speed_up_thr – threshold to detect point with irregular flow  where flow should be recalculated after upscale

See [Tao2012]. And site of project  http://graphics.berkeley.edu/papers/TaoSAN201205/.
Note
 An example using the simpleFlow algorithm can be found at opencv_source_code/samples/cpp/simpleflow_demo.cpp
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 tradeoff 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 8bit singlechannel 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) JeanYves 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 
[Bradski00]  Davis, J.W. and Bradski, G.R. “Motion Segmentation and Pose Recognition with Motion History Gradients”, WACV00, 2000 
[Davis97]  Davis, J.W. and Bobick, A.F. “The Representation and Recognition of Action Using Temporal Templates”, CVPR97, 1997 
[Farneback2003]  Gunnar Farneback, Twoframe motion estimation based on polynomial expansion, Lecture Notes in Computer Science, 2003, (2749), , 363370. 
[Horn81]  Berthold K.P. Horn and Brian G. Schunck. Determining Optical Flow. Artificial Intelligence, 17, pp. 185203, 1981. 
[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. 674679. 
[Welch95]  Greg Welch and Gary Bishop “An Introduction to the Kalman Filter”, 1995 
[Tao2012]  Michael Tao, Jiamin Bai, Pushmeet Kohli and Sylvain Paris. SimpleFlow: A Noniterative, Sublinear Optical Flow Algorithm. Computer Graphics Forum (Eurographics 2012) 
[Zach2007] 
 Zach, T. Pock and H. Bischof. “A Duality Based Approach for Realtime TVL1 Optical Flow”, In Proceedings of Pattern Recognition (DAGM), Heidelberg, Germany, pp. 214223, 2007

[Javier2012]  Javier Sanchez, Enric MeinhardtLlopis and Gabriele Facciolo. “TVL1 Optical Flow Estimation”. 