Articulated human detection with flexible mixtures of parts

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 35, Issue 12, 2013, Pages 2878-2890

  Yang, Yi ^a   Ramanan, Deva ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

articulated shapes; deformable part models; object detection; Pose estimation

Indexed keywords

ARTICULATED SHAPES; DEFORMABLE PART MODELS; HUMAN POSE ESTIMATIONS; OBJECT DETECTION; ORDERS OF MAGNITUDE; PICTORIAL STRUCTURES; POSE ESTIMATION; STATE-OF-THE-ART SYSTEM;

DETECTORS; ENCODING (SYMBOLS); FLEXIBLE STRUCTURES; MIXTURES;

OBJECT RECOGNITION;

ALGORITHM; HUMAN; REPRODUCIBILITY; THEORETICAL MODEL;

ALGORITHMS; HUMANS; MODELS, THEORETICAL; REPRODUCIBILITY OF RESULTS;

EID: 84887598018     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2012.261     Document Type: Article

References (53)

1. P. Felzenszwalb and D. Huttenlocher, "Pictorial Structures for Object Recognition," Intl J. Computer Vision, vol. 61, no. 1, pp. 55-79, 2005.
2. M. Fischler and R. Elschlager, "The Representation and Matching of Pictorial Structures," IEEE Trans. Computers, vol. 22, no. 1, pp. 67-92, Jan. 1973.
3. L. Bourdev and J. Malik, "Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
4. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, "Object Detection with Discriminatively Trained Part-Based Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627-1645, Sept. 2010.
5. P. Felzenszwalb, R. Girshick, and D. McAllester, "Cascade Object Detection with Deformable Part Models," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
6. Y. Yang and D. Ramanan, "Flexible Mixtures of Parts for Articulated Pose Detection, Release 1.3," http://phoenix.ics. uci.edu/software/pose/, 2013.
7. V. Ferrari, M. Marin-Jimenez, and A. Zisserman, "Progressive Search Space Reduction for Human Pose Estimation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
8. V. Ferrari, M. Eichner, M. Marin-Jimenez, and A. Zisserman, "Buffy Stickmen v3.01: Annotated Data and Evaluation Routines for 2D Human Pose Estimation," http://www.robots.ox.ac.uk/vgg/data/stickmen/, 2013.
9. D. Ramanan, "Learning to Parse Images of Articulated Bodies," Proc. Advances in Neural Information Processing System, 2007.
10. Y. Yang and D. Ramanan, "Articulated Pose Estimation with Flexible Mixtures-of-Parts," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011.
11. J. O0Rourke and N. Badler, "Model-Based Image Analysis of Human Motion Using Constraint Propagation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 2, no. 6, pp. 522-536, 1980. (Pubitemid 11463603)
12. D. Hogg, "Model-Based Vision: A Program to See a Walking Person," Image and Vision Computing, vol. 1, no. 1, pp. 5-20, 1983. (Pubitemid 13572232)
13. K. Rohr, "Towards Model-Based Recognition of Human Movements in Image Sequences," CVGIP-Image Understanding, vol. 59, no. 1, pp. 94-115, 1994.
14. D. Ramanan, "Part-Based Models for Finding People and Estimating Their Pose," Visual Analysis of Humans, pp. 199-223, 2011.
15. S. Ioffe and D. Forsyth, "Human Tracking with Mixtures of Trees," Proc. IEEE Intl Conf. Computer Vision, 2001.
16. M. Lee and I. Cohen, "Proposal Maps Driven MCMC for Estimating Human Body Pose in Static Images," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
17. S. Ioffe and D. Forsyth, "Probabilistic Methods for Finding People," Intl J. Computer Vision, vol. 43, no. 1, pp. 45-68, 2001. (Pubitemid 32723477)
18. L. Sigal and M. Black, "Measure Locally, Reason Globally: Occlusion-Sensitive Articulated Pose Estimation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
19. Y. Wang and G. Mori, "Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation," Proc. European Conf. Computer Vision, 2008.
20. T. Tian and S. Sclaroff, "Fast Globally Optimal 2D Human Detection with Loopy Graph Models," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
21. V. Singh, R. Nevatia, and C. Huang, "Efficient Inference with Multiple Heterogenous Part Detectors for Human Pose Estimation," Proc. European Conf. Computer Vision, 2010.
22. X. Lan and D. Huttenlocher, "Beyond Trees: Common-Factor Models for 2D Human Pose Recovery," Proc. IEEE Intl Conf. Computer Vision, 2005.
23. D. Tran and D. Forsyth, "Improved Human Parsing with a Full Relational Model," Proc. European Conf. Computer Vision, 2010.
24. D. Ramanan and C. Sminchisescu, "Training Deformable Models for Localization," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
25. M. Kumar, A. Zisserman, and P. Torr, "Efficient Discriminative Learning of Parts-Based Models," Proc. IEEE Intl Conf. Computer Vision, 2009.
26. B. Sapp, A. Toshev, and B. Taskar, "Cascaded Models for Articulated Pose Estimation," Proc. European Conf. Computer Vision, 2010.
27. M. Andriluka, S. Roth, and B. Schiele, "Pictorial Structures Revisited: People Detection and Articulated Pose Estimation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
28. G. Mori, X. Ren, A. Efros, and J. Malik, "Recovering Human Body Configurations: Combining Segmentation and Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
29. B. Sapp, C. Jordan, and B. Taskar, "Adaptive Pose Priors for Pictorial Structures," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
30. P. Srinivasan and J. Shi, "Bottom-Up Recognition and Parsing of the Human Body," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
31. G. Mori and J. Malik, "Estimating Human Body Configurations Using Shape Context Matching," Proc. European Conf. Computer Vision, 2002.
32. J. Sullivan and S. Carlsson, "Recognizing and Tracking Human Action," Proc. European Conf. Computer Vision, 2002.
33. S. Johnson and M. Everingham, "Combining Discriminative Appearance and Segmentation Cues for Articulated Human Pose Estimation," Proc. IEEE Intl Conf. Computer Vision Workshops, 2009.
34. N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
35. H. Pirsiavash and D. Ramanan, "Steerable Part Models," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2012.
36. W. Yang, Y. Wang, and G. Mori, "Recognizing Human Actions from Still Images with Latent Poses," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
37. M. Sun and S. Savarese, "Articulated Part-Based Model for Joint Object Detection and Pose Estimation," Proc. IEEE Intl Conf. Computer Vision Workshops, 2011.
38. Y. Wang, D. Tran, and D. Liao, and Z. Forsyth, "Discriminative Hierarchical Part-Based Models for Human Parsing and Action Recognition," J. Machine Learning Research, vol. 13, pp. 3075-3102, 2012.
39. B. Epshtein and S. Ullman, "Semantic Hierarchies for Recognizing Objects and Parts," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
40. L. Zhu, Y. Chen, C. Lin, and A. Yuille, "Max Margin Learning of Hierarchical Configural Deformable Templates (HCDTs) for Efficient Object Parsing and Pose Estimation," Intl J. Computer Vision, vol. 93, no. 1, pp. 1-21, 2011.
41. B. Sapp, D. Weiss, and B. Taskar, "Parsing Human Motion with Stretchable Models," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011.
42. D. Park and D. Ramanan, "N-Best Maximal Decoders for Part Models," Proc. IEEE Intl Conf. Computer Vision, 2011.
43. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun, "Support Vector Machine Learning for Interdependent and Structured Output Spaces," Proc. Intl Conf. Machine Learning, 2004.
44. R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin, "Liblinear: A Library for Large Linear Classification," J. Machine Learning Research, vol. 9, pp. 1871-1874, 2008.
45. A. Bordes, L. Bottou, P. Gallinari, and J. Weston, "Solving Multiclass Support Vector Machines with Larank," Proc. Intl Conf. Machine Learning, 2007.
46. D. Ramanan, "Dual Coordinate Descent Solvers for Large Structured Prediction Problems," technical report, Univ. of California, Irvine, 2012.
47. A. Yuille and A. Rangarajan, "The Concave-Convex Procedure," Neural Computation, vol. 15, no. 4, pp. 915-936, 2003. (Pubitemid 37049812)
48. M. Eichner, M. Marin-Jimenez, A. Zisserman, and V. Ferrari, "2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images," Intl J. Computer Vision, vol. 99, no. 2, pp. 190-214, 2012.
49. M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman, "The Pascal Visual Object Classes (VOC) Challenge," Intl J. Computer Vision, vol. 88, no. 2, pp. 303-338, 2010.
50. S. Johnson and M. Everingham, "Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation," Proc. British Machine Vision Conf., 2010.
51. S. Johnson and M. Everingham, "Learning Effective Human Pose Estimation from Inaccurate Annotation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011.
52. Y. Yang and D. Ramanan, "Flexible Mixtures of Parts for Articulated Pose Detection, Release 1.2," http://phoenix.ics. uci.edu/software/pose/, 2013.
53. M. Eichner and V. Ferrari, "Better Appearance Models for Pictorial Structures," Proc. British Machine Vision Conf., 2009.