An eigenbackground subtraction method using recursive error compensation

Authors:
Zhifei Xu;Pengfei Shi;Irene Yu-Hua Gu
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, P.R. China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, P.R. China;Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden
Venue:
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Year:
2006

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Abstract

Eigenbackground subtraction is a commonly used method for moving object detection. The method uses the difference between an input image and the reconstructed background image for detecting foreground objects based on eigenvalue decomposition. In the method, foreground regions are represented in the reconstructed image using eigenbackground in the sense of least mean squared error minimisation. This results in errors that are spread over the entire reconstructed reference image. This will also result in degradation of quality of reconstructed background leading to inaccurate moving object detection. In order to compensate these regions, an efficient method is proposed by using recursive error compensation and an adaptively computed threshold. Experiments were conducted on a range of image sequences with variety of complexity. Performance were evaluated both qualitatively and quantitatively. Comparisons made with two existing methods have shown better approximations of the background images and more accurate detection of foreground objects have been achieved by the proposed method.