Fast Background Modeling and Shadow Removing for Outdoor Surveillance

P. Spagnolo, A. Branc, G. Attolico, and A. Distante (Italy)


Visual Surveillance, Motion Detection, Background Subtraction, Shadow Elimination


Visual surveillance of outdoor environments is one of the most fundamental applications of computer vision. Our context is the visual surveillance of archaeological sites: the goal is the extraction of moving objects, their classification and the interpretation of gestures of the detected human figures, in order to discriminate illegal actions from legal ones. In particular, we focus on the core problem of detecting and extracting moving objects and of eliminating the shadows. The latter step is crucial because shadows can create difficulties to any classifier charged of correctly discriminating the observed moving objects as people, cars, animals, etc. In this work we propose an improvement of a well known algorithm for the detection of moving object, that makes it more robust with respect to different noisy conditions. Moreover, we propose a new approach for removing shadows cast by moving objects. Our idea is to detect shadow regions as background regions that have not substantially changed their texture. We discriminate shadows from moving objects by comparing the photometric gains of neighbouring pixels. The system has been tested on real sequences acquired using a fixed camera into an archaeological site.

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