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(iii) how the background model is updated after each frame? .
Since our background model is updated in a blind fashion, these objects do get incorporated into the background model.
Then, after the object detection, the background model is updated to reflect changes in the observed scene (e.g., lighting changes).
The background model is updated if a pixel is marked as foreground for more than m of the last M frames, in order to compensate for sudden illumination changes and the appearance of static new objects.
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The background model is generated from image sequence and it updates by itself in each process.
The shadow model is updated with a higher probability of 1/4, again due to the less frequent appearance of shadows compared to background.
The model was updated accordingly.
The parameters of the background model are not updated, but could be reestimated if required.
In [13, 14], the techniques are divided into recursive and nonrecursive ones, where recursive methods maintain a single background model that is, updated using each new coming video frame.
The updating strategy of the background model is given as follows: boldsymbol{alpha}_{i+1} = 1-rho boldsymbol{alpha}}_{i} + rhoboldsymbol{alpha}'_{i}, (1-rho boldsymbol{alpha}
To overcome this problem, the long-term background model could be updated selectively.
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