Shannon Entropy as Background Dynamics Estimator In Foreground Detector Algorithms

Foreground segmentation algorithms are sometimes provided with feedback mechanisms to deal with complex scenarios such as dynamic backgrounds. This is accomplished with background dynamic estimators in the case of foreground detectors based on non-parametric models with a historical record of the background. This work introduces the Shannon entropy as a new background dynamics estimator. The paper shows that this approach leads to better figures of merit than those provided by the original background dynamics estimators in state-of-the-art algorithms such as PBAS and SuBSENSE for complex scenarios as dynamic backgrounds or camera jitter in the database ChangeDetection. Also, the Shannon entropy permits to decrease the number of samples in the background model, cutting memory usage, and thus making implementations on embedded devices easier.