Foreground Detection Based on Real-time Background Modeling and Robust Subtraction

This paper presents a robust approach for detecting moving objects from a static background scene that contains slow illumination changes, physical changes and micro- movements. First, we propose a new algorithm for background modeling that adapts to slow illumination and physical changes. This algorithm which is based on pixel state computation and background pixel state decision does not need such training sequences excluding moving objects. Second, we develop an efficient background subtraction algorithm that is able to cope with micro-movement of the background scene. This is done by calculating the similarity between the incoming pixel and its neighborhood pixels in the background model. Finally, we applied this robust approach to some video surveillance sequences of both indoor and outdoor scenes. The results demonstrate the effectiveness of our approach.