:param 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.
:param 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.
:param 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).
:param 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``.
:param 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``.
***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.
:param 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.
:param winSize:window size of optical flow algorithm. Must be not less than ``winSize`` argument of :ocv:func:`calcOpticalFlowPyrLK`. It is needed to calculate required padding for pyramid levels.
:param withDerivatives:set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then :ocv:func:`calcOpticalFlowPyrLK` will calculate them internally.
:param pyrBorder:the border mode for pyramid layers.
:param derivBorder:the border mode for gradients.
:param tryReuseInputImage:put ROI of input image into the pyramid if possible. You can pass ``false`` to force data copying.
:return:number of levels in constructed pyramid. Can be less than ``maxLevel``.
..ocv:function:: 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 )
:param 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.
:param 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.
:param 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.
:param 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.
:param 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``.
***OPTFLOW_FARNEBACK_GAUSSIAN** uses the Gaussian :math:`\texttt{winsize}\times\texttt{winsize}` 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.
:param 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).
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.
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.
:param mask:Output mask image that has the type ``CV_8UC1`` and the same size as ``mhi`` . Its non-zero elements mark pixels where the motion gradient data is correct.
:param orientation:Output motion gradient orientation image that has the same type and the same size as ``mhi`` . Each pixel of the image is a motion orientation, from 0 to 360 degrees.
:param delta2:Maximal (or minimal) allowed difference between ``mhi`` values within a pixel neighborhood. That is, the function finds the minimum ( :math:`m(x,y)` ) and maximum ( :math:`M(x,y)` ) ``mhi`` values over :math:`3 \times 3` neighborhood of each pixel and marks the motion orientation at :math:`(x, y)` as valid only if
:ocv:func:`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.
:param mask:Mask image. It may be a conjunction of a valid gradient mask, also calculated by :ocv:func:`calcMotionGradient` , and the mask of a region whose direction needs to be calculated.
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 :ocv:func:`calcGlobalOrientation` using the extracted mask of the particular component.
:ocv:func:`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()`` .
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
:ocv:func:`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
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`` .
The class implements the algorithm described in P. 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
Threshold defining whether the component is significant enough to be included into the background model ( corresponds to ``TB=1-cf`` 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.
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.
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.
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.
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.
* 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 773-780, 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 651-656, 2004.
:param 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??).
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.
..ocv:member:: 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.
..ocv:member:: int nscales
Number of scales used to create the pyramid of images.
..ocv:member:: 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.
..ocv:member:: 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.
..ocv:member:: int iterations
Stopping criterion iterations number used in the numerical scheme.
..[Farneback2003] Gunnar Farneback, Two-frame motion estimation based on polynomial expansion, Lecture Notes in Computer Science, 2003, (2749), , 363-370.
..[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.
..[Zach2007] C. 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
..[Javier2012] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
