cv::BackgroundSubtractorKNN Class Reference

K-nearest neighbours - based Background/Foreground Segmentation Algorithm. More...

#include <opencv2/video/background_segm.hpp>

Collaboration diagram for cv::BackgroundSubtractorKNN:

Public Member Functions
virtual bool	getDetectShadows () const =0
	Returns the shadow detection flag.
virtual double	getDist2Threshold () const =0
	Returns the threshold on the squared distance between the pixel and the sample.
virtual int	getHistory () const =0
	Returns the number of last frames that affect the background model.
virtual int	getkNNSamples () const =0
	Returns the number of neighbours, the k in the kNN.
virtual int	getNSamples () const =0
	Returns the number of data samples in the background model.
virtual double	getShadowThreshold () const =0
	Returns the shadow threshold.
virtual int	getShadowValue () const =0
	Returns the shadow value.
virtual void	setDetectShadows (bool detectShadows)=0
	Enables or disables shadow detection.
virtual void	setDist2Threshold (double _dist2Threshold)=0
	Sets the threshold on the squared distance.
virtual void	setHistory (int history)=0
	Sets the number of last frames that affect the background model.
virtual void	setkNNSamples (int _nkNN)=0
	Sets the k in the kNN. How many nearest neighbours need to match.
virtual void	setNSamples (int _nN)=0
	Sets the number of data samples in the background model.
virtual void	setShadowThreshold (double threshold)=0
	Sets the shadow threshold.
virtual void	setShadowValue (int value)=0
	Sets the shadow value.
Public Member Functions inherited from cv::BackgroundSubtractor
virtual void	apply (InputArray image, OutputArray fgmask, double learningRate=-1)=0
	Computes a foreground mask.
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

K-nearest neighbours - based Background/Foreground Segmentation Algorithm.

The class implements the K-nearest neighbours background subtraction described in [324] . Very efficient if number of foreground pixels is low.

Member Function Documentation

getDetectShadows()

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

pure virtual

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

Returns the shadow detection flag.

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

getDist2Threshold()

virtual double cv::BackgroundSubtractorKNN::getDist2Threshold ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorKNN.getDist2Threshold(		) ->	retval

Returns the threshold on the squared distance between the pixel and the sample.

The threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to a data sample.

getHistory()

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

pure virtual

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

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

getkNNSamples()

virtual int cv::BackgroundSubtractorKNN::getkNNSamples ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorKNN.getkNNSamples(		) ->	retval

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.

getNSamples()

virtual int cv::BackgroundSubtractorKNN::getNSamples ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorKNN.getNSamples(		) ->	retval

Returns the number of data samples in the background model.

getShadowThreshold()

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

pure virtual

Python:
	cv.BackgroundSubtractorKNN.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::BackgroundSubtractorKNN::getShadowValue ( ) const

pure virtual

Python:
	cv.BackgroundSubtractorKNN.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.

setDetectShadows()

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

pure virtual

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

Enables or disables shadow detection.

setDist2Threshold()

virtual void cv::BackgroundSubtractorKNN::setDist2Threshold ( double  _dist2Threshold )

pure virtual

Python:
	cv.BackgroundSubtractorKNN.setDist2Threshold(	_dist2Threshold	) ->	None

Sets the threshold on the squared distance.

setHistory()

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

pure virtual

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

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

setkNNSamples()

virtual void cv::BackgroundSubtractorKNN::setkNNSamples ( int  _nkNN )

pure virtual

Python:
	cv.BackgroundSubtractorKNN.setkNNSamples(	_nkNN	) ->	None

Sets the k in the kNN. How many nearest neighbours need to match.

setNSamples()

virtual void cv::BackgroundSubtractorKNN::setNSamples ( int  _nN )

pure virtual

Python:
	cv.BackgroundSubtractorKNN.setNSamples(	_nN	) ->	None

Sets the number of data samples in the background model.

The model needs to be reinitalized to reserve memory.

setShadowThreshold()

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

pure virtual

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

Sets the shadow threshold.

setShadowValue()

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

pure virtual

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

Sets the shadow value.

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

• opencv2/video/background_segm.hpp