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Efficient Adaptive Background Subtraction based on Multi-Resolution Background Modelling and Updating

Ruijiang Luo ; Liyuan Li ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling)
to appear in Proc. of Pacific-RIM Conf. on Multimedia (PCM'07), Dec. 11-14, Hong Kong, 2007 (2007)
[Konferensbidrag, refereegranskat]

Adaptive background subtraction (ABS) is a fundamental step for foreground object detection in many real-time video surveillance systems. In many ABS methods, a pixel-based statistical model is used for the background and each pixel is updated online to adapt to various background changes. As a result, heavy computation and memory consumption are required. In this paper, we propose an efficient methodology for implementation of ABS algorithms based on multi-resolution background modelling and sequential sampling for updating background. Experiments and quantitative evaluation are conducted on two open data sets (PETS2001 and PETS2006) and scenarios captured in some public places, and some results are included. Our results have shown that the proposed method requires a significant reduction in memory and CPU usage, meanwhile maintaining a similar foreground segmentation performance as compared with the corresponding single resolution methods.

Nyckelord: Adaptive background subtraction, multi-resolution modelling, principal feature representation, statistical modelling



Denna post skapades 2007-08-21.
CPL Pubid: 45418

 

Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling

Ämnesområden

Bildanalys
Signalbehandling

Chalmers infrastruktur