Evaluation of Background Subtraction Algorithms with Post-Processing

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Evaluation of Background Subtraction Algorithms with Post-Processing
Processing a video stream to segment foreground objects from the background is a critical first step in many computer vision applications. Background subtraction (BGS) is a commonly used technique for achieving this segmentation. The popularity of BGS largely comes from its computational efficiency, which allows applications such as humancomputer interaction, video surveillance, and traffic monitoring to meet their real-time goals. Numerous BGS algorithms and a number of postprocessing techniques that aim to improve the results of these algorithms have been proposed. In this paper, we evaluate several popular, state-of-the-art BGS algorithms and examine how post-processing techniques affect their performance. Our experimental results demonstrate that post-processing techniques can significantly improve the foreground segmentation masks produced by a BGS algorithm. We provide recommendations for achieving robust foreground segmentation based on the lessons learned performing this compa...
Donovan H. Parks, Sidney Fels
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where AVSS
Authors Donovan H. Parks, Sidney Fels
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