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Proceedings Paper

Robust techniques for background subtraction in urban traffic video
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Paper Abstract

Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. There are many challenges in developing a good background subtraction algorithm. First, it must be robust against changes in illumination. Second, it should avoid detecting non-stationary background objects such as swinging leaves, rain, snow, and shadow cast by moving objects. Finally, its internal background model should react quickly to changes in background such as starting and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.

Paper Details

Date Published: 18 January 2004
PDF: 12 pages
Proc. SPIE 5308, Visual Communications and Image Processing 2004, (18 January 2004); doi: 10.1117/12.526886
Show Author Affiliations
Sen-ching S. Cheung, Lawrence Livermore National Lab. (United States)
Chandrika Kamath, Lawrence Livermore National Lab. (United States)


Published in SPIE Proceedings Vol. 5308:
Visual Communications and Image Processing 2004
Sethuraman Panchanathan; Bhaskaran Vasudev, Editor(s)

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