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.
Video Surveillance Pixel State Intensity Stable Interval
Shengshu Wang Gewen Kang Zhi Zhong Ming Yang Pei Chen Yangsheng Xu
Department of Automation University of Electronic Science and Technology of China Chengdu,China;Shen Department of Automation University of Electronic Science and Technology of China Chengdu,China Department of Automation Shanghai Jiao Tong University Shanghai,China;Department of Mechanical and A School of Information Science and Technology Southwest Jiaotong University Chengdu,China;Shenzhen In Shenzhen Institute of Advanced Integration Technology CAS/CUHK,Shenzhen,China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong,Hong Kong;Sh