Articulated pose estimation with flexible mixtures-of-parts

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

Volumn , Issue , 2011, Pages 1385-1392

  Yang, Yi ^a   Ramanan, Deva ^a  

^a Irvine   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MIXTURES; PATTERN RECOGNITION;

ARTICULATED POSE ESTIMATIONS; HUMAN POSE ESTIMATIONS; ORDERS OF MAGNITUDE; POSE ESTIMATION; SPATIAL RELATIONS; STATE-OF-THE-ART SYSTEM; STATIC IMAGES; TREE-STRUCTURED;

DYNAMIC PROGRAMMING;

EID: 80052895150     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995741     Document Type: Conference Paper

References (34)

1. M. Andriluka, S. Roth, and B. Schiele. Pictorial structures revisited: People detection and articulated pose estimation. In Proc. CVPR, volume 1, page 4, 2009.
2. L. Bourdev and J. Malik. Poselets: Body part detectors trained using 3d human pose annotations. In CVPR, pages 1365-1372. IEEE, 2010.
3. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, pages I: 886-893, 2005.
4. M. Eichner, V. Ferrari, and S. Zurich. Better appearance models for pictorial structures. In Proc. BMVC, volume 2, page 6. Citeseer, 2009.
5. B. Epshtein and S. Ullman. Semantic hierarchies for recognizing objects and parts. In CVPR, pages 1-8, 2007.
6. M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman. The PASCAL visual object classes (VOC) challenge. IJCV, 88(2):303-338, 2010.
7. P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. IJCV, 61(1):55-79, 2005.
8. P. Felzenszwalb and D. McAllester. Object detection grammars. Technical report, University of Chicago, 2010.
9. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. IEEE PAMI, 99(1), 5555.
10. V. Ferrari, M. Marin-Jimenez, and A. Zisserman. Progressive search space reduction for human pose estimation. In CVPR, June 2008.
11. T. Finley and T. Joachims. Training structural SVMs when exact inference is intractable. In Proceedings of the 25th international conference on Machine learning, pages 304-311. ACM New York, NY, USA, 2008.
12. M. Fischler and R. Elschlager. The representation and matching of pictorial structures. Computers, IEEE Transactions on, 100(1):67-92, 1973.
13. D. Hogg. Model-based vision: a program to see a walking person. Image and Vision computing, 1(1):5-20, 1983.
14. S. Johnson and M. Everingham. Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation. In Proc. BMVC, 2010.
15. S. Johnson and M. Everingham. Combining discriminative appearance and segmentation cues for articulated human pose estimation. In ICCV Workshops, pages 405-412, 2010.
16. M. Kumar, A. Zisserman, and P. Torr. Efficient discriminative learning of parts-based models. In CVPR, pages 552-559. IEEE, 2010.
17. X. Lan and D. Huttenlocher. Beyond trees: Common-factor models for 2d human pose recovery. In CVPR, volume 1, pages 470-477. IEEE, 2005.
18. M. Lee and I. Cohen. Proposal maps driven mcmc for estimating human body pose in static images. In CVPR, volume 2. IEEE, 2004.
19. B. Leibe, A. Leonardis, and B. Schiele. Combined object categorization and segmentation with an implicit shape model. In ECCV04 workshop on statistical learning in computer vision, pages 17-32. Citeseer, 2004.
20. D. Marr and H. Nishihara. Representation and recognition of the spatial organization of three-dimensional shapes. Proceedings of the Royal Society of London. Series B, Biological Sciences, 200(1140):269-294, 1978.
21. G. Mori, X. Ren, A. Efros, and J. Malik. Recovering human body configurations: Combining segmentation and recognition. In CVPR, 2004.
22. J. O'Rourke and N. Badler. Model-based image analysis of human motion using constraint propagation. PAMI, 2(6):522-536, 1980.
23. D. Ramanan. Learning to parse images of articulated bodies. NIPS, 19:1129, 2007.
24. D. Ramanan and C. Sminchisescu. Training deformable models for localization. In CVPR, volume 1, pages 206-213. IEEE, 2006.
25. K. Rohr. Towards model-based recognition of human movements in image sequences. CVGIP-Image Understanding, 59(1):94-115, 1994.
26. B. Sapp, C. Jordan, and B. Taskar. Adaptive pose priors for pictorial structures. In CVPR, pages 422-429. IEEE, 2010.
27. B. Sapp, A. Toshev, and B. Taskar. Cascaded Models for Articulated Pose Estimation. ECCV 2010, pages 406-420, 2010.
28. L. Sigal and M. Black. Measure locally, reason globally: Occlusion-sensitive articulated pose estimation. In CVPR, volume 2, pages 2041-2048. IEEE, 2006.
29. V. Singh, R. Nevatia, and C. Huang. Efficient inference with multiple heterogenous part detectors for human pose estimation. In ECCV, 2010.
30. P. Srinivasan and J. Shi. Bottom-up recognition and parsing of the human body. In CVPR, 2007.
31. T. Tian and S. Sclaroff. Fast globally optimal 2D human detection with loopy graph models. In CVPR, pages 81-88, 2010.
32. D. Tran and D. Forsyth. Improved Human Parsing with a Full Relational Model. ECCV, pages 227-240, 2010.
33. Y. Wang and G. Mori. Multiple tree models for occlusion and spatial constraints in human pose estimation. ECCV, pages 710-724, 2008.
34. A. Yuille and A. Rangarajan. The concave-convex procedure. Neural Computation, 15(4):915-936, 2003.