The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Bayesian human segmentation in crowded situations
Abstract
The problem of segmenting individual humans in crowded situations from stationary video camera sequences is exacerbated by object inter-occlusion. We pose this problem as a "model-based segmentation" problem in which human shape models are used to interpret the foreground in a Bayesian framework. The solution is obtained by using an efficient Markov chain Monte Carlo (MCMC) method that uses domain knowledge as proposal probabilities. Knowledge of various aspects including human shape, human height, camera model, and image cues including human head candidates, foreground/background separation are integrated in one theoretically sound framework. We show promising results and evaluations on some challenging data.
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Cites
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
Pfinder: real-time tracking of the human body
Wren, C. R., Azarbayejani, A., Darrell, T., & Pentland, A. P. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598236 · Cited by 4,169
W/sup 4/: real-time surveillance of people and their activities
Haritaoglu, I., Harwood, D., & Davis, L. S. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868683 · Cited by 2,523
Distance transformations in digital images
Borgefors, G. (1986) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(86)80047-0 · Cited by 1,898
Example-based object detection in images by components
Mohan, A., Papageorgiou, C., & Poggio, T. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.917571 · Cited by 948
Real-time object detection for "smart" vehicles
Gavrila, D. M., & Philomin, V. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.791202 · Cited by 630
BraMBLe: a Bayesian multiple-blob tracker
Isard, M., & MacCormick, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937594 · Cited by 628
Image segmentation by data-driven markov chain monte carlo
Tu, Z., & Zhu, S.-C. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000239 · Cited by 565
Markov chain concepts related to sampling algorithms
(1995) · Markov Chain Monte Carlo in Practice · doi:10.1201/b14835-8 · Cited by 311
Mittal, A., & Davis, L. S. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-47969-4_2 · Cited by 203
Cited by
Floor Fields for Tracking in High Density Crowd Scenes
Ali, S., & Shah, M. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88688-4_1 · Cited by 388
Crowd Analysis Using Computer Vision Techniques
Silveira Jacques Junior, J., Musse, S., & Jung, C. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.937394 · Cited by 343
Visual-Based Human Crowds Behavior Analysis Based on Graph Modeling and Matching
Chen, D.-Y., & Huang, P.-C. (2013) · IEEE Sensors Journal · doi:10.1109/jsen.2013.2245889 · Cited by 31
Haq, E. U., Huarong, X., Xuhui, C., Wanqing, Z., Jianping, F., & Abid, F. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08167-y · Cited by 12
Metadata
- Authors
- Zhao, T., & Nevatia, R.
- Venue
- 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
- Publisher
- IEEE Comput. Soc
- Published
- 2003-12-22
- Volume
- 2
- Pages
- II-459-66
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2101178587
- Cited by
- 294
- References
- 13
- FWCI
- 8.64
- Type
- conference paper
- Language
- en
- Open access
- closed
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