Multiple granularity analysis for fine-grained action detection

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

Volumn , Issue , 2014, Pages 756-763

  Ni, Bingbing ^a   Paramathayalan, Vignesh R ^a   Moulin, Pierre ^b  

^a ADVANCED DIGITAL SCIENCES CENTER   (Singapore)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

action detection; interaction tracking; multiple granularity

Indexed keywords

COMPUTER SCIENCE; COMPUTERS; ELECTRICAL ENGINEERING; PATTERN RECOGNITION; SOFTWARE ENGINEERING;

ANALYSIS PROBLEMS; COMPACT FEATURES; CONTEXTUAL INFORMATION; FUSION STRATEGIES; MULTIPLE GRANULARITY; SEQUENTIAL TASK; SPATIO TEMPORAL; STATE-OF-THE-ART METHODS;

PALMPRINT RECOGNITION;

EID: 84911397627     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.102     Document Type: Conference Paper

References (32)

1. http://www.murase.m.is.nagoya-u.ac.jp/kscgr/index.html.
2. J. Berclaz, F. Fleuret, E. Turetken, and P. Fua. Multiple object tracking using k-shortest paths optimization. T-PAMI, 33(9):1806-1819, 2011.
3. S. Blackman. Multiple hypothesis tracking for multiple target tracking. IEEE Aerospace and Electronic Systems Magazine, 19(1):5-18, 2004.
4. C. Chang, R. Ansari, and A. Khokhar. Multiple object tracking with kernel particle filter. In CVPR, volume 1, pages 566-573, 2005.
5. H.-T. Chen, H.-H. Lin, and T.-L. Liu. Multi-object tracking using dynamical graph matching. In CVPR, volume 2, pages 210-217, 2001.
6. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
7. M. de La Gorce, N. Paragios, and D. Fleet. Model-based hand tracking with texture, shading and self-occlusions. In CVPR, 2008.
8. H. Grabner, M. Grabner, and H. Bischof. Quicktime reference guide, quicktime cd. In BMVC, pages 47-56, 2006.
9. M. Han, W. Xu, H. Tao, and Y. Gong. An algorithm for multiple object trajectory tracking. In CVPR, volume 1, pages 864-871, 2004.
10. T. Joachims, T. Finley, and C.-N. Yu. Cutting-plane training of structural svms. Machine Learning, 77(1):27-59, 2009.
11. Z. Kalal, J. Matas, and K. Mikolajczyk. P-n learning: Bootstrapping binary classifiers by structural constraints. In CVPR, pages 49-56, 2010.
12. H. Kjellström, J. Romero, D. Martínez, and D. Kragić. Simultaneous visual recognition of manipulation actions and manipulated objects. In ECCV, pages 336-349, 2008.
13. H. S. Koppula, R. Gupta, and A. Saxena. Learning human activities and object affordances from rgb-d videos. CoRR, 2012.
14. I. Laptev and T. Lindeberg. Space-time interest points. In ICCV, 2003.
15. J. Lei, X. Ren, and D. Fox. Fine-grained kitchen activity recognition using rgb-d. In ACM Conference on Ubiquitous Computing, pages 208-211, 2012.
16. C. Li and K. M. Kitani. Pixel-level hand detection in ego-centric videos. In CVPR, 2013.
17. J. MacCormick and A. Blake. A probabilistic exclusion principle for tracking multiple objects. In ICCV, volume 1, pages 572-578, 1999.
18. D. Moore, I. Essa, and M. Hayes. Exploiting human actions and object context for recognition tasks. In ICCV, Corfu, Greece, 1999.
19. K. P. Murphy, Y. Weiss, and M. I. Jordan. Loopy belief propagation for approximate inference: An empirical study. In UAI, pages 467-475, 1999.
20. J. C. Niebles, H. Wang, and L. Fei-fei. Unsupervised learning of human action categories using spatial-temporal words. In BMVC, 2006.
21. B. Packer and D. Koller. A combined pose, object, and feature model for action understanding. In CVPR, 2012.
22. M. Rohrbach, S. Amin, M. Andriluka, and B. Schiele. A database for fine grained activity detection of cooking activities. In CVPR, 2012.
23. M. Rohrbach, S. Amin, M. Andriluka, and B. Schiele. A database for fine grained activity detection of cooking activities. In CVPR, pages 1194-1201, 2012.
24. M. S. Ryoo and J. Aggarwal. Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities. In ICCV, pages 1593-1600, 2009.
25. H. Trinh, Q. Fan, P. Gabbur, and S. Pankanti. Hand tracking by binary quadratic programming and its application to retail activity recognition. In CVPR, pages 1902-1909, 2012.
26. H. Wang, A. Kläser, C. Schmid, and L. Cheng-Lin. Action recognition by dense trajectories. In CVPR, pages 3169-3176, 2011.
27. Y. Wang and G. Mori. Hidden part models for human action recognition: Probabilistic versus max margin. T-PAMI, 33(7):1310-1323, 2011.
28. J. Wu, A. Osuntogun, T. Choudhury, M. Philipose, and J. Rehg. A scalable approach to activity recognition based on object use. In ICCV, 2007.
29. C. Yang, R. Duraiswami, and L. Davis. Fast multiple object tracking via a hierarchical particle filter. In ICCV, volume 1, pages 212-219, 2005.
30. B. Yao, A. Khosla, and L. Fei-fei. Classifying actions and measuring action similarity by modeling the mutual context of objects and human poses. In ICML, 2011.
31. J. Yuan, Z. Liu, and Y. Wu. Discriminative video pattern search for efficient action detection. T-PAMI, 33(9):1728-1743, 2011.
32. T. Zhao and R. Nevatia. Tracking multiple humans in crowded environment. In CVPR, volume 2, pages 406-413, 2004.