2007 IEEE International Conference on Image Processing - San Antonio, Texas, U.S.A. - September 16-19, 2007

Technical Program

Paper Detail

Paper:WA-L2.4
Session:Video Object Segmentation and Tracking II
Time:Wednesday, September 19, 10:50 - 11:10
Presentation: Lecture
Title: BACKGROUND SUBTRACTION USING INCREMENTAL SUBSPACE LEARNING
Authors: Lu Wang; Tsinghua University 
 Lei Wang; Tsinghua University 
 Ming Wen; Tsinghua University 
 Qing Zhuo; Tsinghua University 
 Wenyuan Wang; Tsinghua University 
Abstract: Background modeling and subtraction is a basic component of many computer vision and video analysis applications. It has a critical impact on the performance of object tracking and activity analysis. In this paper, we propose an effective and adaptive background modeling and subtraction approach that can deal with dynamic scenes. The proposed approach uses a subspace learning method to model the background and the subspace is updated on-line with a sequential Karhunen-Loeve algorithm. A linear prediction model is also used to make the detection more robust. Experimental results demonstrate that the proposed approach is able to model the background and detect moving objects under various type of background scenarios with close to real-time performance.



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