Paper
18 May 2004 Gibbs distributions and Markov random field model: application on background modeling in video surveillance
Lihua Guo, Jianhua Li, Liya Chen, Shutang Yang
Author Affiliations +
Proceedings Volume 5297, Real-Time Imaging VIII; (2004) https://doi.org/10.1117/12.524665
Event: Electronic Imaging 2004, 2004, San Jose, California, United States
Abstract
Recently, backgrounds modeling methods that employ Time-Adaptive, Per Pixel, and Mixture of Gaussians (TAPPMOG) model have become more and more popular owing to their intrinsic appealing properties in video surveillance. Nevertheless, they are not able parse to monitor global changes in the scene, because they model the background as a set of independent pixel processes. In this paper, Gibbs Distributions-Markov Random Field (GDMRF) model is applied to the background modeling, and then the Simulated Annealing algorithm is developed to extract the background from video sequences. Experimental comparison between our methods and a classic pixel-based approach reveals that our proposed method is really effective in recovering from situations of sudden global illumination changes of the background, and can perfectly adapt the object moving in the background.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lihua Guo, Jianhua Li, Liya Chen, and Shutang Yang "Gibbs distributions and Markov random field model: application on background modeling in video surveillance", Proc. SPIE 5297, Real-Time Imaging VIII, (18 May 2004); https://doi.org/10.1117/12.524665
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KEYWORDS
Video surveillance

Video

Algorithms

Algorithm development

Magnetorheological finishing

Computer vision technology

Image processing

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