Huang, K. and Wang, L. and Tan, T. and Maybank, Stephen J. (2008) A real-time object detecting and tracking system for outdoor night surveillance. Pattern Recognition 41 (1), pp. 432-444. ISSN 0031-3203.Full text not available from this repository.
Autonomous video surveillance and monitoring has a rich history. Many deployed systems are able to reliably track human motion in indoor and controlled outdoor environments. However, object detection and tracking at night remain very important problems for visual surveillance. The objects are often distant, small and their signatures have low contrast against the background. Traditional methods based on the analysis of the difference between successive frames and a background frame will do not work. In this paper, a novel real time object detection algorithm is proposed for night-time visual surveillance. The algorithm is based on contrast analysis. In the first stage, the contrast in local change over time is used to detect potential moving objects. Then motion prediction and spatial nearest neighbor data association are used to suppress false alarms. Experiments on real scenes show that the algorithm is effective for night-time object detection and tracking.
|Keyword(s) / Subject(s):||Visual surveillance, night, contrast, detection and tracking|
|School or Research Centre:||Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Informatics|
|Date Deposited:||02 Feb 2011 12:40|
|Last Modified:||17 Apr 2013 12:18|
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