Part-based multiple-person tracking with partial occlusion handling

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2012, Pages 1815-1821

  Shu, Guang ^a   Dehghan, Afshin ^a   Oreifej, Omar ^a   Hand, Emily ^b   Shah, Mubarak ^a  

^a UNIVERSITY OF CENTRAL FLORIDA   (United States)

^b UNIVERSITY OF NEVADA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DETECTION PERFORMANCE; DETECTION STAGE; HUMAN BODIES; HUMAN DETECTION; PARTIAL OCCLUSIONS; PROBABILITY OF DETECTION; STATE-OF-THE-ART PERFORMANCE; SVM CLASSIFIERS;

ELECTRICAL ENGINEERING; PATTERN RECOGNITION; SOFTWARE ENGINEERING;

COMPUTER VISION;

EID: 84866648554     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247879     Document Type: Conference Paper

References (23)

1. S. Avidan. Ensemble tracking. In PAMI, 2010.
2. B. Babenko, M. Yang, and M. Hsuan. Visual tracking with online multiple instance learning. In PAMI, 2010.
3. B. Benfold and I. Reid. Stable multi-target tracking in realtime surveillance video. In CVPR, 2011.
4. M. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier, and L. V. Gool. Robust tracking-by-detection using a detector confidence particle filter. In ICCV, 2009.
5. W. Brendel, M. Amer, and S. Todorovic. Multiobject tracking as maximum weight independent set. In CVPR, 2011.
6. R. Collins and Y. Liu. Online selection of discriminative tracking features. In ICCV, 2003.
7. D. Comaniciu, V. Ramesh, and P. Meer. Kernel-based object tracking. In PAMI, 2003.
8. T. B. Dinh, N. Vo, and G. Medioni. Context tracker: Exploring supporters and distracters in unconstrained environments. In CVPR, 2011.
9. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. In PAMI, 2010.
10. H. Grabner, M. Grabner, and H. Bischof. Real-time tracking via on-line boosting. In BMVC, 2006.
11. Z. Kalal. P-n learning?: Bootstrapping binary classifiers by structural constraints. In ICCV, 2010.
12. R. Kasturi, D. Goldgof, P. Soundararajan, V. Manohar, J. Garofolo, M. Boonstra, V. Korzhova, and J. Zhang. Framework for performance evaluation for face, text and vehicle detection and tracking in video: data, metrics, and protocol. In PAMI, 2009.
13. C. Kuo, C. Huang, and R. Nevatia. Multi-target tracking by on-line learned discriminative appearance models. In CVPR, 2010.
14. L. Leal-Taixe, G. Pons-Moll, and B. Rosenhahn. Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker. In ICCV Workshop on Modeling, Simulation and Visual Analysis of Large Crowds, 2011.
15. S. Pellegrini, A. Ess, and L. van Gool. Improving data association by joint modeling of pedestrian trajectories and groupings. In ECCV, 2010.
16. J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers. MIT Press, 1999.
17. K. Rangarajan and M. Shah. Establishing motion correspondence. In Graphics and Image Processing: Image Understanding, 1991.
18. M. Rodriguez, I. Laptev, J. Sivic, and J.-Y. Audibert. Density-aware person detection and tracking in crowds. In ICCV, 2011.
19. J. Shi and C. Tomasi. Good features to track. In CVPR, 1994.
20. X.Wang and T. Han. An hog-lbp human detector with partial occlusion handling. In ICCV, 2009.
21. B. Wu and R. Nevatia. Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. In International Journal of Computer Vision, 2007.
22. K. Yamaguchi, A. Berg, L. Ortiz, and T. Berg. Who are you with and where are you going? In CVPR, 2011.
23. L. Zhang, Y. Li, and R. Nevatia. Global data association for multi-object tracking using network flows. In CVPR, 2008.