Adaptive Foreground Extraction for Crowd Analytics Surveillance on Unconstrained Environments
نویسندگان
چکیده
Background modeling is one of the key steps in any visual surveillance system. A good background modeling algorithm should be able to detect objects/targets under any environmental condition. The influence of illumination variance has been a major challenge in many background modeling algorithms. These algorithms produce poor object segmentation or consume substantial amount of computational time, which makes them not implementable at real time. In this paper we propose a novel background modeling method based on Gaussian Mixture Method (GMM). The proposed method uses Phase Congruency (PC) edge features to overcome the effect of illumination variance, while preserving efficient background/foreground segmentation. Moreover, our method uses a combination of pixel information of GMM and the Phase texture information of PC, to construct a foreground invariant of the illumination variance.
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