Evaluation of Background Subtraction Algorithms with Post-Processing

TitleEvaluation of Background Subtraction Algorithms with Post-Processing
Publication TypeConference Paper
Year of Publication2008
AuthorsParks, D. H., and S. S. Fels
Conference NameAdvanced Video and Signal Based Surveillance, 2008. AVSS '08. IEEE 5th International Conference on
Pagination192 -199
Date Publishedsep.
Keywordsbackground subtraction algorithms, computer vision, foreground objects segmentation, image segmentation, video signal processing, video stream 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 human-computer interaction, video surveillance, and traffic monitoring to meet their real-time goals. Numerous BGS algorithms and a number of post-processing 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 comparative study.


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