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The Base Class for Background/Foreground Segmentation The class is only used to define the common interface for the whole family of background/foreground segmentation algorithms. */ class CV_EXPORTS_W BackgroundSubtractor : public Algorithm { public: //! the virtual destructor virtual ~BackgroundSubtractor(); //! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image. CV_WRAP_AS(apply) virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=0); //! computes a background image virtual void getBackgroundImage(OutputArray backgroundImage) const; }; /*! Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm The class implements the following algorithm: "An improved adaptive background mixture model for real-time tracking with shadow detection" P. KadewTraKuPong and R. Bowden, Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001." http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf */ class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor { public: //! the default constructor CV_WRAP BackgroundSubtractorMOG(); //! the full constructor that takes the length of the history, the number of gaussian mixtures, the background ratio parameter and the noise strength CV_WRAP BackgroundSubtractorMOG(int history, int nmixtures, double backgroundRatio, double noiseSigma=0); //! the destructor virtual ~BackgroundSubtractorMOG(); //! the update operator virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=0); //! re-initiaization method virtual void initialize(Size frameSize, int frameType); virtual AlgorithmInfo* info() const; protected: Size frameSize; int frameType; Mat bgmodel; int nframes; int history; int nmixtures; double varThreshold; double backgroundRatio; double noiseSigma; }; /*! The class implements the following algorithm: "Improved adaptive Gausian mixture model for background subtraction" Z.Zivkovic International Conference Pattern Recognition, UK, August, 2004. http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf */ class CV_EXPORTS_W BackgroundSubtractorMOG2 : public BackgroundSubtractor { public: //! the default constructor CV_WRAP BackgroundSubtractorMOG2(); //! the full constructor that takes the length of the history, the number of gaussian mixtures, the background ratio parameter and the noise strength CV_WRAP BackgroundSubtractorMOG2(int history, float varThreshold, bool bShadowDetection=true); //! the destructor virtual ~BackgroundSubtractorMOG2(); //! the update operator virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=-1); //! computes a background image which are the mean of all background gaussians virtual void getBackgroundImage(OutputArray backgroundImage) const; //! re-initiaization method virtual void initialize(Size frameSize, int frameType); virtual AlgorithmInfo* info() const; protected: Size frameSize; int frameType; Mat bgmodel; Mat bgmodelUsedModes;//keep track of number of modes per pixel int nframes; int history; int nmixtures; //! here it is the maximum allowed number of mixture components. //! Actual number is determined dynamically per pixel double varThreshold; // threshold on the squared Mahalanobis distance to decide if it is well described // by the background model or not. Related to Cthr from the paper. // This does not influence the update of the background. A typical value could be 4 sigma // and that is varThreshold=4*4=16; Corresponds to Tb in the paper. ///////////////////////// // less important parameters - things you might change but be carefull //////////////////////// float backgroundRatio; // corresponds to fTB=1-cf from the paper // TB - threshold when the component becomes significant enough to be included into // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0. // For alpha=0.001 it means that the mode should exist for approximately 105 frames before // it is considered foreground // float noiseSigma; float varThresholdGen; //correspondts to Tg - threshold on the squared Mahalan. dist. to decide //when a sample is close to the existing components. If it is not close //to any a new component will be generated. I use 3 sigma => Tg=3*3=9. //Smaller Tg leads to more generated components and higher Tg might make //lead to small number of components but they can grow too large float fVarInit; float fVarMin; float fVarMax; //initial variance for the newly generated components. //It will will influence the speed of adaptation. A good guess should be made. //A simple way is to estimate the typical standard deviation from the images. //I used here 10 as a reasonable value // min and max can be used to further control the variance float fCT;//CT - complexity reduction prior //this is related to the number of samples needed to accept that a component //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get //the standard Stauffer&Grimson algorithm (maybe not exact but very similar) //shadow detection parameters bool bShadowDetection;//default 1 - do shadow detection unsigned char nShadowDetection;//do shadow detection - insert this value as the detection result - 127 default value float fTau; // Tau - shadow threshold. The shadow is detected if the pixel is darker //version of the background. Tau is a threshold on how much darker the shadow can be. //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003. }; /** * Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1) * images of the same size, where 255 indicates Foreground and 0 represents Background. * This class implements an algorithm described in "Visual Tracking of Human Visitors under * Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere, * A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012. */ class CV_EXPORTS BackgroundSubtractorGMG: public cv::BackgroundSubtractor { public: BackgroundSubtractorGMG(); virtual ~BackgroundSubtractorGMG(); virtual AlgorithmInfo* info() const; /** * Validate parameters and set up data structures for appropriate image size. * Must call before running on data. * @param frameSize input frame size * @param min minimum value taken on by pixels in image sequence. Usually 0 * @param max maximum value taken on by pixels in image sequence. e.g. 1.0 or 255 */ void initialize(cv::Size frameSize, double min, double max); /** * Performs single-frame background subtraction and builds up a statistical background image * model. * @param image Input image * @param fgmask Output mask image representing foreground and background pixels * @param learningRate Determines how quickly features are "forgotten" from histograms */ virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=-1.0); /** * Releases all inner buffers. */ void release(); //! Total number of distinct colors to maintain in histogram. int maxFeatures; //! Set between 0.0 and 1.0, determines how quickly features are "forgotten" from histograms. double learningRate; //! Number of frames of video to use to initialize histograms. int numInitializationFrames; //! Number of discrete levels in each channel to be used in histograms. int quantizationLevels; //! Prior probability that any given pixel is a background pixel. A sensitivity parameter. double backgroundPrior; //! Value above which pixel is determined to be FG. double decisionThreshold; //! Smoothing radius, in pixels, for cleaning up FG image. int smoothingRadius; //! Perform background model update bool updateBackgroundModel; private: double maxVal_; double minVal_; cv::Size frameSize_; int frameNum_; cv::Mat_<int> nfeatures_; cv::Mat_<unsigned int> colors_; cv::Mat_<float> weights_; cv::Mat buf_; }; } #endif