Motion Analysis

Classes
class	cv::BackgroundSubtractor
	Base class for background/foreground segmentation. : More...
class	cv::BackgroundSubtractorKNN
	K-nearest neighbours - based Background/Foreground Segmentation Algorithm. More...
class	cv::BackgroundSubtractorMOG2
	Gaussian Mixture-based Background/Foreground Segmentation Algorithm. More...

Functions
Ptr< BackgroundSubtractorKNN >	cv::createBackgroundSubtractorKNN (int history=500, double dist2Threshold=400.0, bool detectShadows=true)
	Creates KNN Background Subtractor. More...
Ptr< BackgroundSubtractorMOG2 >	cv::createBackgroundSubtractorMOG2 (int history=500, double varThreshold=16, bool detectShadows=true)
	Creates MOG2 Background Subtractor. More...

Detailed Description

Function Documentation

createBackgroundSubtractorKNN()

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

Python:
	retval	=	cv.createBackgroundSubtractorKNN(	[, history[, dist2Threshold[, detectShadows]]]	)

Creates KNN Background Subtractor.

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.

createBackgroundSubtractorMOG2()

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

Python:
	retval	=	cv.createBackgroundSubtractorMOG2(	[, history[, varThreshold[, detectShadows]]]	)

Creates MOG2 Background Subtractor.

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.

Examples:

samples/cpp/segment_objects.cpp.