Gaussian Mixture-based Background/Foreground Segmentation Algorithm. More...

#include <opencv2/video/background_segm.hpp>

Collaboration diagram for cv::BackgroundSubtractorMOG2:

Public Member Functions
virtual void	apply (InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE=0
	Computes a foreground mask.

virtual double	getBackgroundRatio () const =0
	Returns the "background ratio" parameter of the algorithm.

virtual double	getComplexityReductionThreshold () const =0
	Returns the complexity reduction threshold.

virtual bool	getDetectShadows () const =0
	Returns the shadow detection flag.

virtual int	getHistory () const =0
	Returns the number of last frames that affect the background model.

virtual int	getNMixtures () const =0
	Returns the number of gaussian components in the background model.

virtual double	getShadowThreshold () const =0
	Returns the shadow threshold.

virtual int	getShadowValue () const =0
	Returns the shadow value.

virtual double	getVarInit () const =0
	Returns the initial variance of each gaussian component.

virtual double	getVarMax () const =0

virtual double	getVarMin () const =0

virtual double	getVarThreshold () const =0
	Returns the variance threshold for the pixel-model match.

virtual double	getVarThresholdGen () const =0
	Returns the variance threshold for the pixel-model match used for new mixture component generation.

virtual void	setBackgroundRatio (double ratio)=0
	Sets the "background ratio" parameter of the algorithm.

virtual void	setComplexityReductionThreshold (double ct)=0
	Sets the complexity reduction threshold.

virtual void	setDetectShadows (bool detectShadows)=0
	Enables or disables shadow detection.

virtual void	setHistory (int history)=0
	Sets the number of last frames that affect the background model.

virtual void	setNMixtures (int nmixtures)=0
	Sets the number of gaussian components in the background model.

virtual void	setShadowThreshold (double threshold)=0
	Sets the shadow threshold.

virtual void	setShadowValue (int value)=0
	Sets the shadow value.

virtual void	setVarInit (double varInit)=0
	Sets the initial variance of each gaussian component.

virtual void	setVarMax (double varMax)=0

virtual void	setVarMin (double varMin)=0

virtual void	setVarThreshold (double varThreshold)=0
	Sets the variance threshold for the pixel-model match.

virtual void	setVarThresholdGen (double varThresholdGen)=0
	Sets the variance threshold for the pixel-model match used for new mixture component generation.

Public Member Functions inherited from cv::BackgroundSubtractor
virtual void	getBackgroundImage (OutputArray backgroundImage) const =0
	Computes a background image.

Public Member Functions inherited from cv::Algorithm
	Algorithm ()

virtual	~Algorithm ()

virtual void	clear ()
	Clears the algorithm state.

virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.

virtual String	getDefaultName () const

virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.

virtual void	save (const String &filename) const

void	write (const Ptr< FileStorage > &fs, const String &name=String()) const

virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.

void	write (FileStorage &fs, const String &name) const

Additional Inherited Members
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file.

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

Gaussian Mixture-based Background/Foreground Segmentation Algorithm.

The class implements the Gaussian mixture model background subtraction described in [325] and [324] .

Member Function Documentation

◆ apply()

virtual void cv::BackgroundSubtractorMOG2::apply	(	InputArray	image,
		OutputArray	fgmask,
		double	learningRate = `-1`
	)

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.apply(	image[, fgmask[, learningRate]]	) ->	fgmask

Computes a foreground mask.

Parameters

image	Next video frame. Floating point frame will be used without scaling and should be in range \([0,255]\).
fgmask	The output foreground mask as an 8-bit binary image.
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.

Implements cv::BackgroundSubtractor.

◆ getBackgroundRatio()

virtual double cv::BackgroundSubtractorMOG2::getBackgroundRatio ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getBackgroundRatio(		) ->	retval

Returns the "background ratio" parameter of the 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.

◆ getComplexityReductionThreshold()

virtual double cv::BackgroundSubtractorMOG2::getComplexityReductionThreshold ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getComplexityReductionThreshold(		) ->	retval

Returns the complexity reduction threshold.

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.

◆ getDetectShadows()

virtual bool cv::BackgroundSubtractorMOG2::getDetectShadows ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getDetectShadows(		) ->	retval

Returns the shadow detection flag.

If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for details.

◆ getHistory()

virtual int cv::BackgroundSubtractorMOG2::getHistory ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getHistory(		) ->	retval

Returns the number of last frames that affect the background model.

◆ getNMixtures()

virtual int cv::BackgroundSubtractorMOG2::getNMixtures ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getNMixtures(		) ->	retval

Returns the number of gaussian components in the background model.

◆ getShadowThreshold()

virtual double cv::BackgroundSubtractorMOG2::getShadowThreshold ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getShadowThreshold(		) ->	retval

Returns the shadow threshold.

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 Cucchiara, Detecting Moving Shadows...*, IEEE PAMI,2003.

◆ getShadowValue()

virtual int cv::BackgroundSubtractorMOG2::getShadowValue ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getShadowValue(		) ->	retval

Returns the shadow value.

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.

◆ getVarInit()

virtual double cv::BackgroundSubtractorMOG2::getVarInit ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getVarInit(		) ->	retval

Returns the initial variance of each gaussian component.

◆ getVarMax()

virtual double cv::BackgroundSubtractorMOG2::getVarMax ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getVarMax(		) ->	retval

◆ getVarMin()

virtual double cv::BackgroundSubtractorMOG2::getVarMin ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getVarMin(		) ->	retval

◆ getVarThreshold()

virtual double cv::BackgroundSubtractorMOG2::getVarThreshold ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getVarThreshold(		) ->	retval

Returns the variance threshold for the pixel-model match.

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.

◆ getVarThresholdGen()

virtual double cv::BackgroundSubtractorMOG2::getVarThresholdGen ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.getVarThresholdGen(		) ->	retval

Returns the variance threshold for the pixel-model match used for new mixture component generation.

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.

◆ setBackgroundRatio()

virtual void cv::BackgroundSubtractorMOG2::setBackgroundRatio ( double ratio )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setBackgroundRatio(	ratio	) ->	None

Sets the "background ratio" parameter of the algorithm.

◆ setComplexityReductionThreshold()

virtual void cv::BackgroundSubtractorMOG2::setComplexityReductionThreshold ( double ct )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setComplexityReductionThreshold(	ct	) ->	None

Sets the complexity reduction threshold.

◆ setDetectShadows()

virtual void cv::BackgroundSubtractorMOG2::setDetectShadows ( bool detectShadows )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setDetectShadows(	detectShadows	) ->	None

Enables or disables shadow detection.

◆ setHistory()

virtual void cv::BackgroundSubtractorMOG2::setHistory ( int history )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setHistory(	history	) ->	None

Sets the number of last frames that affect the background model.

◆ setNMixtures()

virtual void cv::BackgroundSubtractorMOG2::setNMixtures ( int nmixtures )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setNMixtures(	nmixtures	) ->	None

Sets the number of gaussian components in the background model.

The model needs to be reinitalized to reserve memory.

◆ setShadowThreshold()

virtual void cv::BackgroundSubtractorMOG2::setShadowThreshold ( double threshold )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setShadowThreshold(	threshold	) ->	None

Sets the shadow threshold.

◆ setShadowValue()

virtual void cv::BackgroundSubtractorMOG2::setShadowValue ( int value )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setShadowValue(	value	) ->	None

Sets the shadow value.

◆ setVarInit()

virtual void cv::BackgroundSubtractorMOG2::setVarInit ( double varInit )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setVarInit(	varInit	) ->	None

Sets the initial variance of each gaussian component.

◆ setVarMax()

virtual void cv::BackgroundSubtractorMOG2::setVarMax ( double varMax )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setVarMax(	varMax	) ->	None

◆ setVarMin()

virtual void cv::BackgroundSubtractorMOG2::setVarMin ( double varMin )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setVarMin(	varMin	) ->	None

◆ setVarThreshold()

virtual void cv::BackgroundSubtractorMOG2::setVarThreshold ( double varThreshold )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setVarThreshold(	varThreshold	) ->	None

Sets the variance threshold for the pixel-model match.

◆ setVarThresholdGen()

virtual void cv::BackgroundSubtractorMOG2::setVarThresholdGen ( double varThresholdGen )

pure virtual

Python:
	cv.BackgroundSubtractorMOG2.setVarThresholdGen(	varThresholdGen	) ->	None

Sets the variance threshold for the pixel-model match used for new mixture component generation.

The documentation for this class was generated from the following file:

opencv2/video/background_segm.hpp

Public Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ apply()

◆ getBackgroundRatio()

◆ getComplexityReductionThreshold()

◆ getDetectShadows()

◆ getHistory()

◆ getNMixtures()

◆ getShadowThreshold()

◆ getShadowValue()

◆ getVarInit()

◆ getVarMax()

◆ getVarMin()

◆ getVarThreshold()

◆ getVarThresholdGen()

◆ setBackgroundRatio()

◆ setComplexityReductionThreshold()

◆ setDetectShadows()

◆ setHistory()

◆ setNMixtures()

◆ setShadowThreshold()

◆ setShadowValue()

◆ setVarInit()

◆ setVarMax()

◆ setVarMin()

◆ setVarThreshold()

◆ setVarThresholdGen()