Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper.
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#include <opencv2/bgsegm.hpp>
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.
virtual void cv::bgsegm::BackgroundSubtractorGSOC::apply |
( |
InputArray |
image, |
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|
OutputArray |
fgmask, |
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double |
learningRate = -1 |
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) |
| |
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pure virtual |
Python: |
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| fgmask | = | cv.bgsegm_BackgroundSubtractorGSOC.apply( | image[, fgmask[, learningRate]] | ) |
Computes a foreground mask.
- 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. |
Implements cv::BackgroundSubtractor.
virtual void cv::bgsegm::BackgroundSubtractorGSOC::getBackgroundImage |
( |
OutputArray |
backgroundImage | ) |
const |
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pure virtual |
Python: |
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| backgroundImage | = | cv.bgsegm_BackgroundSubtractorGSOC.getBackgroundImage( | [, backgroundImage] | ) |
Computes a background image.
- Parameters
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backgroundImage | The output background image. |
- Note
- Sometimes the background image can be very blurry, as it contain the average background statistics.
Implements cv::BackgroundSubtractor.
The documentation for this class was generated from the following file: