The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Segmentation and Tracking of Multiple Humans in Crowded Environments
Abstract
Segmentation and tracking of multiple humans in crowded situations is made difficult by interobject occlusion. We propose a model based approach to interpret the image observations by multiple, partially occluded human hypotheses in a Bayesian framework. We define a joint image likelihood for multiple humans based on the appearance of the humans, the visibility of body obtained by occlusion reasoning, and foreground/background separation. The optimal solution is obtained by using an efficient sampling method, data-driven Markov chain Monte Carlo (DDMCMC), which uses image observations for proposal probabilities. Knowledge of various aspects including human shape, camera model, and image cues are integrated in one theoretically sound framework. We present experimental results and quantitative evaluation, demonstrating that the resulting approach is effective for very challenging data.
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Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
Cited by
Feature Mining for Localised Crowd Counting
Chen, K., Loy, C. C., Gong, S., & Xiang, T. (2012) · Procedings of the British Machine Vision Conference 2012 · doi:10.5244/c.26.21 · Cited by 666
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation
Pham, V.-Q., Kozakaya, T., Yamaguchi, O., & Okada, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.372 · Cited by 363
Real-time visual tracking using compressive sensing
Li, H., Shen, C., & Shi, Q. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995483 · Cited by 297
Crowd Counting with Deep Negative Correlation Learning
Shi, Z., Zhang, L., Liu, Y., Cao, X., Ye, Y., Cheng, M.-M., & Zheng, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00564 · Cited by 283
Human detection from images and videos: A survey
Nguyen, D. T., Li, W., & Ogunbona, P. O. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2015.08.027 · Cited by 267
Metadata
- Authors
- Zhao, T., Nevatia, R., & Wu, B.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2008-07
- Volume
- 30
- Issue
- 7
- Pages
- 1198-1211
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2096246546
- Cited by
- 371
- References
- 5
- FWCI
- 47.25
- Type
- journal article
- Language
- en
- Open access
- closed
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