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A MRF-based approach for real-time subway monitoring
Abstract
There has been an increase in the use of video surveillance and monitoring in public areas to improve safety and security. Change detection and crowding/congestion density estimation are two sub-tasks in a subway monitoring system. We propose a method that decomposes this problem into two steps. The first step consists of a change detection algorithm that distinguishes the background from the foreground. This is done using a discontinuity preserving MRF-based approach where the information from different sources (background subtraction, intensity modeling) is combined with spatial constraints to provide a smooth motion detection map. Then, the obtained change detection map is combined with a geometry module that performs a soft auto-calibration to estimate a measure of congestion of the observed area (platform). Extensive experimental results in a metro station of a metropolitan city demonstrates the performance and the potential of our method.
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Cited by
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Kolmogorov, V., & Zabih, R. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1262177 · Cited by 3,127
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Ryan, D., Denman, S., Fookes, C., & Sridharan, S. (2009) · 2009 Digital Image Computing: Techniques and Applications · doi:10.1109/dicta.2009.22 · Cited by 309
Metadata
- Authors
- Paragios, N., & Ramesh, V.
- Venue
- Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
- Publisher
- IEEE Comput. Soc
- Published
- 2005-08-25
- Volume
- 1
- Pages
- I-1034-I-1040
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2158979073
- Cited by
- 252
- References
- 0
- FWCI
- 12.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Paragios, N., & Ramesh, V. (2005). A MRF-based approach for real-time subway monitoring. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. https://doi.org/10.1109/cvpr.2001.990644
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