Class BackgroundSubtractorGSOC
java.lang.Object
org.opencv.core.Algorithm
org.opencv.video.BackgroundSubtractor
org.opencv.bgsegm.BackgroundSubtractorGSOC
Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper.
This algorithm demonstrates better performance on CDNET 2014 dataset compared to other algorithms in OpenCV.
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Field Summary
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic BackgroundSubtractorGSOC__fromPtr__(long addr) voidComputes a foreground mask.voidComputes a foreground mask.voidComputes a foreground mask with known foreground mask input.voidComputes a foreground mask with known foreground mask input.voidgetBackgroundImage(Mat backgroundImage) Computes a background image.Methods inherited from class Algorithm
clear, empty, getDefaultName, getNativeObjAddr, save
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Constructor Details
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BackgroundSubtractorGSOC
protected BackgroundSubtractorGSOC(long addr)
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Method Details
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__fromPtr__
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apply
Description copied from class:BackgroundSubtractorComputes a foreground mask.- Overrides:
applyin classBackgroundSubtractor- Parameters:
image- Next video frame.fgmask- The output foreground mask as an 8-bit binary image.learningRate- The value between 0 and 1 that indicates how fast the background model is learnt. Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means that the background model is not updated at all, 1 means that the background model is completely reinitialized from the last frame.
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apply
Description copied from class:BackgroundSubtractorComputes a foreground mask.- Overrides:
applyin classBackgroundSubtractor- Parameters:
image- Next video frame.fgmask- The output foreground mask as an 8-bit binary image. learnt. Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means that the background model is not updated at all, 1 means that the background model is completely reinitialized from the last frame.
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apply
Description copied from class:BackgroundSubtractorComputes a foreground mask with known foreground mask input.- Overrides:
applyin classBackgroundSubtractor- Parameters:
image- Next video frame. Floating point frame will be used without scaling and should be in range \([0,255]\).knownForegroundMask- The mask for inputting already known foreground, allows model to ignore pixels.fgmask- The output foreground mask as an 8-bit binary image.learningRate- The value between 0 and 1 that indicates how fast the background model is learnt. Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means that the background model is not updated at all, 1 means that the background model is completely reinitialized from the last frame. Note: This method has a default virtual implementation that throws a "not impemented" error. Foreground masking may not be supported by all background subtractors.
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apply
Description copied from class:BackgroundSubtractorComputes a foreground mask with known foreground mask input.- Overrides:
applyin classBackgroundSubtractor- Parameters:
image- Next video frame. Floating point frame will be used without scaling and should be in range \([0,255]\).knownForegroundMask- The mask for inputting already known foreground, allows model to ignore pixels. learnt. Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means that the background model is not updated at all, 1 means that the background model is completely reinitialized from the last frame. Note: This method has a default virtual implementation that throws a "not impemented" error. Foreground masking may not be supported by all background subtractors.fgmask- The output foreground mask as an 8-bit binary image.
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getBackgroundImage
Description copied from class:BackgroundSubtractorComputes a background image.- Overrides:
getBackgroundImagein classBackgroundSubtractor- Parameters:
backgroundImage- The output background image. Note: Sometimes the background image can be very blurry, as it contain the average background statistics.
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