Background Segmentation
cuda::BackgroundSubtractorMOG
Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
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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].
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
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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.
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cuda::BackgroundSubtractorMOG2
Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
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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].
cuda::createBackgroundSubtractorMOG2
Creates MOG2 Background Subtractor
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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.
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cuda::BackgroundSubtractorGMG
Background/Foreground Segmentation Algorithm.
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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
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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.
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cuda::BackgroundSubtractorFGD
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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;
};
cuda::BackgroundSubtractorFGD::getForegroundRegions
Returns the output foreground regions calculated by findContours().
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C++: void cuda::BackgroundSubtractorFGD::getForegroundRegions(OutputArrayOfArrays foreground_regions)
Params foreground_regions: |
| Output array (CPU memory). |
cuda::createBackgroundSubtractorFGD
Creates FGD Background Subtractor
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C++: Ptr<cuda::BackgroundSubtractorGMG> cuda::createBackgroundSubtractorFGD(const FGDParams& params=FGDParams())
Parameters: |
- params – Algorithm’s parameters. See [FGD2003] for explanation.
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[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
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[MOG2004] |
- Zivkovic. Improved adaptive Gausian mixture model for background subtraction. International Conference Pattern Recognition, UK, August, 2004
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[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
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