Classes
class
cv::BackgroundSubtractor
Base class for background/foreground segmentation. :
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class
cv::BackgroundSubtractorKNN
K-nearest neighbours - based Background/Foreground Segmentation
Algorithm
.
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class
cv::BackgroundSubtractorMOG2
Gaussian Mixture-based Background/Foreground Segmentation
Algorithm
.
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Functions
Ptr
<
BackgroundSubtractorKNN
>
cv::createBackgroundSubtractorKNN
(int history=500, double dist2Threshold=400.0, bool detectShadows=true)
Creates KNN Background Subtractor.
Ptr
<
BackgroundSubtractorMOG2
>
cv::createBackgroundSubtractorMOG2
(int history=500, double varThreshold=16, bool detectShadows=true)
Creates MOG2 Background Subtractor.
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createBackgroundSubtractorKNN()
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Python:
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cv.createBackgroundSubtractorKNN(
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[, history[, dist2Threshold[, detectShadows]]]
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) ->
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retval
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#include <
opencv2/video/background_segm.hpp
>
Creates KNN Background Subtractor.
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Parameters
-
historyLength of the history.
dist2ThresholdThreshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update.
detectShadowsIf 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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Python:
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cv.createBackgroundSubtractorMOG2(
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[, history[, varThreshold[, detectShadows]]]
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) ->
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retval
|
#include <
opencv2/video/background_segm.hpp
>
Creates MOG2 Background Subtractor.
-
Parameters
-
historyLength of the history.
varThresholdThreshold 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.
detectShadowsIf 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.
