Efficient Adaptive Background Subtraction based on Multi-Resolution Background Modelling and Updating
Paper i proceeding, 2007

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.

Adaptive background subtraction

multi-resolution modelling

statistical modelling

principal feature representation


Ruijiang Luo

Liyuan Li

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

to appear in Proc. of Pacific-RIM Conf. on Multimedia (PCM'07), Dec. 11-14, Hong Kong, 2007



Datorseende och robotik (autonoma system)

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