Markovian framework for foreground-background-shadow separation of real world video scenes

Authors:
Csaba Benedek;Tamás Szirányi
Affiliations:
Department of Information Technology, Pázmány Péter Catholic University, Budapest, Hungary;Analogical Computing Laboratory, Computer and Automation Institute, Hungarian Academy of Sciences, Budapest, Hungary
Venue:
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Year:
2006

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Abstract

In this paper we give a new model for foreground-back-ground-shadow separation. Our method extracts the faithful silhouettes of foreground objects even if they have partly background like colors and shadows are observable on the image. It does not need any a priori information about the shapes of the objects, it assumes only they are not point-wise. The method exploits temporal statistics to characterize the background and shadow, and spatial statistics for the foreground. A Markov Random Field model is used to enhance the accuracy of the separation. We validated our method on outdoor and indoor video sequences captured by the surveillance system of the university campus, and we also tested it on well-known benchmark videos.