:param templateImage:single-channel template image; ``CV_8U`` or ``CV_32F`` array.
@ -167,9 +166,13 @@ Finds the geometric transform (warp) between two images in terms of the ECC crit
:param warpMatrix:floating-point :math:`2\times 3` or :math:`3\times 3` mapping matrix (warp).
:param motionType:parameter, specifying the type of motion:
* **MOTION_TRANSLATION** sets a translational motion model; ``warpMatrix`` is :math:`2\times 3` with the first :math:`2\times 2` 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 :math:`2\times 3`.
* **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated; ``warpMatrix`` is :math:`2\times 3`.
* **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are estimated;``warpMatrix`` is :math:`3\times 3`.
:param 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.
@ -177,13 +180,13 @@ Finds the geometric transform (warp) between two images in terms of the ECC crit
The function estimates the optimum transformation (``warpMatrix``) with respect to ECC criterion ([EP08]_), that is
:param fgmask:The output foreground mask as an 8-bit binary image.
:param 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.
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
The class implements the algorithm described in [KB2001]_.
:param 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.
..ocv:class:: BackgroundSubtractorMOG2 : public BackgroundSubtractor
:param 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.
Here are important members of the class that control the algorithm, which you can set after constructing the class instance:
..ocv:member:: int nmixtures
BackgroundSubtractorMOG2::getHistory
--------------------------------------
Returns the number of last frames that affect the background model
Maximum allowed number of mixture components. Actual number is determined dynamically per pixel.
..ocv:function:: int BackgroundSubtractorMOG2::getHistory() const
..ocv:member:: float backgroundRatio
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.
BackgroundSubtractorMOG2::setHistory
--------------------------------------
Sets the number of last frames that affect the background model
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.
..ocv:member:: float fVarInit
BackgroundSubtractorMOG2::getNMixtures
--------------------------------------
Returns the number of gaussian components in the background model
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.
..ocv:function:: int BackgroundSubtractorMOG2::getNMixtures() const
..ocv:member:: float fVarMin
Parameter used to further control the variance.
BackgroundSubtractorMOG2::setNMixtures
--------------------------------------
Sets the number of gaussian components in the background model
Returns the "background ratio" parameter of the algorithm
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.
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.
The value for marking shadow pixels in the output foreground mask. Default value is 127.
BackgroundSubtractorMOG2::setBackgroundRatio
---------------------------------------------
Sets the "background ratio" parameter of the 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.
BackgroundSubtractorMOG2::getVarThresholdGen
---------------------------------------------
Returns the variance scale factor for the pixel-model match
The class implements the Gaussian mixture model background subtraction described in:
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.
* 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.
BackgroundSubtractorMOG2::setVarThresholdGen
---------------------------------------------
Sets the variance scale factor for the pixel-model match
* 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??).
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 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.
See :ocv:funcx:`BackgroundSubtractor::operator()`.
See :ocv:func:`BackgroundSubtractor::getBackgroundImage`.
A shadow is detected if the 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.
..[Davis97] Davis, J.W. and Bobick, A.F. “The Representation and Recognition of Action Using Temporal Templates”, CVPR97, 1997
..[EP08] 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] 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
..[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
@ -768,6 +824,6 @@ Releases all inner buffers.
..[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".
..[Zivkovic2004] 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.
..[EP08] Evangelidis, G.D. and Psarakis E.Z. "Parametric Image Alignment using Enhanced Correlation Coefficient Maximization", IEEE Transactions on PAMI, vol. 32, no. 10, 2008
..[Zivkovic2006] 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.
Vadim Pisarevsky
12 years ago