Action recognition in cluttered dynamic scenes using pose-specific part models

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 113-120

  Singh, Vivek Kumar ^a   Nevatia, Ram ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION MODELS; ACTION RECOGNITION; COMPLEX BACKGROUND; DATA SETS; DYNAMIC BACKGROUND; DYNAMIC SCENES; HUMAN ACTIONS; JOINT TRACKING AND RECOGNITION; KEYPOSES; MULTIPLE VIEWPOINTS; OCCLUSION CONSTRAINT; TRAINING DATA; TRANSFORMATION MATRICES; TREE-STRUCTURE;

IMAGE RECOGNITION; LINEAR TRANSFORMATIONS; MOTION ESTIMATION;

THREE DIMENSIONAL;

EID: 84856661036     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126232     Document Type: Conference Paper

References (31)

1. M. Andriluka, S. Roth, and B. Schiele. Pictorial structures revisited: People detection and articulated pose estimation. In CVPR, pages 1014-1021, 2009. 1, 2, 4, 5
2. M. Andriluka, S. Roth, and B. Schiele. Monocular 3d pose estimation and tracking by detection. In CVPR, pages 623 -630, 2010. 2
3. M. Collins. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In EMNLP, 2002. 3
4. P. F. Felzenszwalb and D. P. Huttenlocher. Pictorial structures for object recognition. IJCV, 61(1):55-79, 2005. 1, 2, 4, 5, 6
5. V. Ferrari, M. J. Marín-Jiménez, and A. Zisserman. Pose search: Retrieving people using their pose. In CVPR, 2009. 2, 5
6. Y. Hu, L. Cao, F. Lv, S. Yan, Y. Gong, and T. Huang. Action detection in complex scenes with spatial and temporal ambiguities. In ICCV, pages 128 -135, 2009. 2
7. C. Huang and R. Nevatia. High performance object detection by collaborative learning of joint ranking of granules features. In CVPR, pages 41-48, 2010. 3
8. N. Ikizler and D. A. Forsyth. Searching video for complex activities with finite state models. In CVPR, 2007. 2
9. H. Jiang and D. Martin. Global pose estimation using non-tree models. In CVPR, 2008. 1, 2, 4
10. S. X. Ju, M. J. Black, and Y. Yacoob. Cardboard people: A parameterized model of articulated image motion. In FG, 1996. 4
11. D. Koller and N. Friedman. Probabilistic Graphical Models - Principles and Techniques. MIT Press, 2009. 5
12. X. Lan and D. P. Huttenlocher. Beyond trees: Common-factor models for 2d human pose recovery. ICCV, 2005. 2
13. I. Laptev. On space-time interest points. International Journal on Computer Vision (IJCV), 64(2-3):107-123, 2005. 2, 6
14. F. Lv and R. Nevatia. Single view human action recognition using key pose matching and viterbi path searching. In CVPR, 2007. 2, 3
15. R. Messing, C. Pal, and H. Kautz. Activity recognition using the velocity histories of tracked keypoints. In ICCV, 2009. 2
16. L.-P. Morency, A. Quattoni, and T. Darrell. Latent-dynamic discriminative models for continuous gesture recognition. In CVPR, 2007. 2
17. P. Natarajan and R. Nevatia. View and scale invariant action recognition using multiview shape-flow models. In CVPR, 2008. 1, 2, 6, 7, 8
18. P. Natarajan, V. K. Singh, and R. Nevatia. Learning 3d action models from a few 2d videos for view invariant action recognition. In CVPR, 2010. 1, 2, 6, 7, 8
19. H. Ning,W. Xu, Y. Gong, and T. S. Huang. Latent pose estimator for continuous action recognition. In ECCV (2), 2008. 2
20. D. Ramanan. Learning to parse images of articulated bodies. In NIPS, pages 1129-1136, 2007. 1, 2, 4, 6
21. R. Rosales and S. Sclaroff. Inferring body pose without tracking body parts. In CVPR, 2000. 2
22. L. Sigal, A. O. Balan, and M. J. Black. Combined discriminative and generative articulated pose and non-rigid shape estimation. In NIPS, 2007. 2, 3
23. L. Sigal and M. J. Black. Measure locally, reason globally: Occlusion-sensitive articulated pose estimation. In CVPR, pages 2041-2048, 2006. 2, 6
24. C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas. Conditional random fields for contextual human motion recognition. In ICCV, pages 1808-1815, 2005. 2
25. J. Sullivan and S. Carlsson. Recognizing and tracking human action. In ECCV (1), pages 629-644, 2002. 3
26. C. Sutton, K. Rohanimanesh, and A. McCallum. Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. In ICML, page 99, 2004. 3
27. T.-P. Tian and S. Sclaroff. Fast globally optimal 2d human detection with loopy graph models. In CVPR, 2010. 1, 2, 4
28. R. Urtasun, D. J. Fleet, and P. Fua. 3d people tracking with gaussian process dynamical models. In CVPR, 2006. 2
29. Y.Wang and G. Mori. Multiple tree models for occlusion and spatial constraints in human pose estimation. In ECCV, 2008. 2
30. X. K. Wei and J. Chai. Modeling 3d human poses from uncalibrated monocular images. In ICCV, pages 1873-1880, 2009. 3, 6
31. D. Weinland, E. Boyer, and R. Ronfard. Action recognition from arbitrary views using 3d exemplars. In ICCV, 2007. 2