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,...

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Bibliographic Details
Published in2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance pp. 192 - 199
Main Authors Parks, D.H., Fels, S.S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2008
Subjects
Application software
background subtraction
Computational efficiency
Computer vision
Computerized monitoring
Object recognition
post-processing
real time vision
Robustness
Signal processing
Streaming media
Video surveillance
Videoconference
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Summary: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.
ISBN:0769533418
9780769533414
DOI:10.1109/AVSS.2008.19