Package org.opencv.video

Class BackgroundSubtractorKNN

java.lang.Object
org.opencv.core.Algorithm
org.opencv.video.BackgroundSubtractor
org.opencv.video.BackgroundSubtractorKNN
public class BackgroundSubtractorKNN extends BackgroundSubtractor
K-nearest neighbours - based Background/Foreground Segmentation Algorithm. The class implements the K-nearest neighbours background subtraction described in CITE: Zivkovic2006 . Very efficient if number of foreground pixels is low.

  • Method Details

    • __fromPtr__

      public static BackgroundSubtractorKNN __fromPtr__(long addr)

    • getHistory

      public int getHistory()
      Returns the number of last frames that affect the background model
      Returns:
      automatically generated

    • setHistory

      public void setHistory(int history)
      Sets the number of last frames that affect the background model
      Parameters:
      history - automatically generated

    • getNSamples

      public int getNSamples()
      Returns the number of data samples in the background model
      Returns:
      automatically generated

    • setNSamples

      public void setNSamples(int _nN)
      Sets the number of data samples in the background model. The model needs to be reinitalized to reserve memory.
      Parameters:
      _nN - automatically generated

    • getDist2Threshold

      public double getDist2Threshold()
      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.
      Returns:
      automatically generated

    • setDist2Threshold

      public void setDist2Threshold(double _dist2Threshold)
      Sets the threshold on the squared distance
      Parameters:
      _dist2Threshold - automatically generated

    • getkNNSamples

      public int getkNNSamples()
      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.
      Returns:
      automatically generated

    • setkNNSamples

      public void setkNNSamples(int _nkNN)
      Sets the k in the kNN. How many nearest neighbours need to match.
      Parameters:
      _nkNN - automatically generated

    • getDetectShadows

      public boolean getDetectShadows()
      Returns the shadow detection flag If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for details.
      Returns:
      automatically generated

    • setDetectShadows

      public void setDetectShadows(boolean detectShadows)
      Enables or disables shadow detection
      Parameters:
      detectShadows - automatically generated

    • getShadowValue

      public int getShadowValue()
      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.
      Returns:
      automatically generated

    • setShadowValue

      public void setShadowValue(int value)
      Sets the shadow value
      Parameters:
      value - automatically generated

    • getShadowThreshold

      public double getShadowThreshold()
      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.
      Returns:
      automatically generated

    • setShadowThreshold

      public void setShadowThreshold(double threshold)
      Sets the shadow threshold
      Parameters:
      threshold - automatically generated