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
K-nearest neigbours - based Background/Foreground Segmentation Algorithm.
.. ocv: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.
:param 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.
: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.
BackgroundSubtractorKNN::getHistory
--------------------------------------
Returns the number of last frames that affect the background model
.. ocv:function:: int BackgroundSubtractorKNN::getHistory() const
BackgroundSubtractorKNN::setHistory
--------------------------------------
Sets the number of last frames that affect the background model
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.
.. ocv:function:: int BackgroundSubtractorKNN::getkNNSamples() const
BackgroundSubtractorKNN::setkNNSamples
---------------------------------------------
Sets the k in the kNN. How many nearest neigbours need to match.
.. ocv:function:: 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.
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.
Background Subtractor module based on the algorithm given in [Gold2012]_.
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@@ -974,9 +1108,9 @@ Releases all inner buffers.
.. [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
.. [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
.. [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
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@@ -990,7 +1124,7 @@ Releases all inner buffers.
.. [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
.. [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 Non-iterative, Sublinear Optical Flow Algorithm. Computer Graphics Forum (Eurographics 2012)
