Background Segmentation

cuda::BackgroundSubtractorMOG

Gaussian Mixture-based Background/Foreground Segmentation Algorithm.

class cuda::BackgroundSubtractorMOG : public cv::BackgroundSubtractorMOG

The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [MOG2001].

See also

BackgroundSubtractorMOG

Note

• An example on gaussian mixture based background/foreground segmantation can be found at opencv_source_code/samples/gpu/bgfg_segm.cpp

cuda::createBackgroundSubtractorMOG

Creates mixture-of-gaussian background subtractor

C++: Ptr<cuda::BackgroundSubtractorMOG> cuda::createBackgroundSubtractorMOG(int history=200, int nmixtures=5, double backgroundRatio=0.7, double noiseSigma=0)

Parameters:

• history – Length of the history.
• nmixtures – Number of Gaussian mixtures.
• backgroundRatio – Background ratio.
• noiseSigma – Noise strength (standard deviation of the brightness or each color channel). 0 means some automatic value.

cuda::BackgroundSubtractorMOG2

Gaussian Mixture-based Background/Foreground Segmentation Algorithm.

class cuda::BackgroundSubtractorMOG2 : public cv::BackgroundSubtractorMOG2

The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [MOG2004].

See also

BackgroundSubtractorMOG2

cuda::createBackgroundSubtractorMOG2

Creates MOG2 Background Subtractor

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

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.

cuda::BackgroundSubtractorGMG

Background/Foreground Segmentation Algorithm.

class cuda::BackgroundSubtractorGMG : public cv::BackgroundSubtractorGMG

The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [GMG2012].

cuda::createBackgroundSubtractorGMG

Creates GMG Background Subtractor

C++: Ptr<cuda::BackgroundSubtractorGMG> cuda::createBackgroundSubtractorGMG(int initializationFrames=120, double decisionThreshold=0.8)

Parameters:

• initializationFrames – Number of frames of video to use to initialize histograms.
• decisionThreshold – Value above which pixel is determined to be FG.

cuda::BackgroundSubtractorFGD

class cuda::BackgroundSubtractorFGD : public cv::BackgroundSubtractor

The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [FGD2003].

class CV_EXPORTS BackgroundSubtractorFGD : public cv::BackgroundSubtractor
{
public:
    virtual void getForegroundRegions(OutputArrayOfArrays foreground_regions) = 0;
};

See also

BackgroundSubtractor

cuda::BackgroundSubtractorFGD::getForegroundRegions

Returns the output foreground regions calculated by findContours().

C++: void cuda::BackgroundSubtractorFGD::getForegroundRegions(OutputArrayOfArrays foreground_regions)

Params foreground_regions:  Output array (CPU memory).

cuda::createBackgroundSubtractorFGD

Creates FGD Background Subtractor

C++: Ptr<cuda::BackgroundSubtractorGMG> cuda::createBackgroundSubtractorFGD(const FGDParams& params=FGDParams())

Parameters:

• params – Algorithm’s parameters. See [FGD2003] for explanation.

[FGD2003]

(1, 2) Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian. Foreground Object Detection from Videos Containing Complex Background. ACM MM2003 9p, 2003.

[MOG2001]

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

[MOG2004]

Zivkovic. Improved adaptive Gausian mixture model for background subtraction. International Conference Pattern Recognition, UK, August, 2004

[GMG2012]

Godbehere, A. Matsukawa and K. Goldberg. Visual Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive Audio Art Installation. American Control Conference, Montreal, June 2012