A novel robust background modeling algorithm for complex natural scenes

Authors:
Zhi-Ling Wang;Zong-Hai Chen;Hui-Yong Chen
Affiliations:
University of Science and Technology of China, Hefei, China;1. University of Science and Technology of China/ 2. Institute of Automation, Chinese Academy of Sciences , 1.Hefei/ 2.Beijing, China;University of Science and Technology of China, Hefei, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

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Abstract

Background modeling is fundamentally important in the computer vision tasks such as image understanding, object tracking and video surveillance. It is especially difficult in the complex natural scenes, mainly due to two matters: 1) gross errors resulted by random outliers that can not be described in a uniform distribution; 2) structural confusion cluttered by sample sets' polymorphism, which is originated by multiple structures. For dealing with these problems, a novel robust background modeling algorithm is presented. The model is established by an improved Multi-RANSAC approach for dynamic background pixels and by one-tail trimmed sample mean estimator for static pixels. A three-component-set is derived for the model so that it can be updated quickly in a unified framework for both types. It stands right even when there are more than 70 percent outliers and is fit for complex natural scenes. Quantitative evaluation and comparisons with traditional methods show that the proposed method has much improved results.