Silhouette representation and matching for 3D pose discrimination - A comparative study

Image and Vision Computing

Volumn 28, Issue 4, 2010, Pages 654-667

  Chen, Cheng ^a   Zhuang, Yueting ^a   Xiao, Jun ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Comparative study; Motion recovery; Performance evaluation; Pose inferring; Shape analysis

Indexed keywords

LEARNING ALGORITHMS;

COMPARATIVE STUDIES; DATA SETS; DESCRIPTORS; DISCRIMINATIVE METHODS; HUMAN POSE; MATCHING ALGORITHM; MOTION RECOVERY; POSE DISCRIMINATION; POSE MAPPING; PRACTICAL SYSTEMS; QUANTITATIVE EVALUATION; REPRESENTATION METHOD; SHAPE ANALYSIS; SHAPE REPRESENTATION AND MATCHING; SYSTEMATIC STUDY; TRAINING EXAMPLE; YAW ANGLES;

THREE DIMENSIONAL;

EID: 74749093808     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2009.10.008     Document Type: Article

References (80)

1. J. Deutscher, A. Blake, I.D. Reid, Articulated body motion capture by annealed particle filtering, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2000, pp. 2126-2133.
2. Zhao X., and Liu Y. Generative tracking of 3d human motion by hierarchical annealed genetic algorithm. Pattern Recognition 41 8 (2008) 2470-2483
3. A. Fathi, G. Mori, Human pose estimation using motion exemplars, in: IEEE International Conference on Computer Vision, 2007, pp. 1-8.
4. C. Sminchisescu, B. Triggs, Covariance scaled sampling for monocular 3d body tracking, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2001, pp. 447-454.
5. Lee M.W., and Nevatia R. Human pose tracking in monocular sequence using multilevel structured models. IEEE Transactions on Pattern Analysis and Machine Intelligence 31 1 (2009) 27-38
6. L. Sigal, S. Bhatia, S. Roth, M.J. Black, M. Isard, Tracking loose-limbed people, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2004, pp. 421-428.
7. Y. Wu, G. Hua, T. Yu, Tracking articulated body by dynamic markov network, in: IEEE International Conference on Computer Vision, 2003, pp. 1094-1101.
8. G. Mori, J. Malik, Recovering 3d human body configurations using shape contexts, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (7) (2006) 1052-1062. doi:http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.149.
9. A.M. Elgammal, C.-S. Lee, Inferring 3d body pose from silhouettes using activity manifold learning, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2004, pp. 681-688.
10. Ren L., Shakhnarovich G., Hodgins J.K., Pfister H., and Viola P.A. Learning silhouette features for control of human motion. ACM Transactions on Graphics 24 4 (2005) 1303-1331
11. Agarwal A., and Triggs B. Recovering 3d human pose from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 1 (2006) 44-58
12. H. Ning, W. Xu, Y. Gong, T.S. Huang, Discriminative learning of visual words for 3d human pose estimation, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2008.
13. F. Lv, R. Nevatia, Single view human action recognition using key pose matching and viterbi path searching, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.
14. Ong E.-J., Micilotta A.S., Bowden R., and Hilton A. Viewpoint invariant exemplar-based 3d human tracking. Computer Vision and Image Understanding 104 2-3 (2006) 178-189
15. R. Poppe, Evaluating example-based pose estimation: experiments on the humaneva sets, in: Online Proceedings of the Workshop on Evaluation of Articulated Human Motion and Pose Estimation (EHuM) at IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, MN, 2007, pp. 1-8.
16. G. Shakhnarovich, P.A. Viola, T. Darrell, Fast pose estimation with parameter-sensitive hashing, in: IEEE International Conference on Computer Vision, 2003, pp. 750-759.
17. R. Rosales, S. Sclaroff, Inferring body pose without tracking body parts, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2000, pp. 721-727.
18. Howe N.R. Silhouette lookup for monocular 3d pose tracking. Image and Vision Computing 25 3 (2007) 331-341
19. Loncaric S. A survey of shape analysis techniques. Pattern Recognition 31 8 (1998) 983-1001
20. Zhang D., and Lu G. Review of shape representation and description techniques. Pattern Recognition 37 1 (2004) 1-19
21. Malik J., Belongie S., Leung T.K., and Shi J. Contour and texture analysis for image segmentation. International Journal of Computer Vision 43 1 (2001) 7-27
22. Y. Zhuang, C. Chen, Efficient silhouette extraction with dynamic viewpoint, in: IEEE International Conference on Computer Vision, 2007, pp. 1-8.
23. C. Sanderson, D. Gibbins, On classifying silhouettes in adverse conditions, in: Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004, pp. 173-178.
24. Zhang D., and Lu G. A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval. Journal of Visual Communication and Image Representation 14 1 (2003) 39-57
25. R. Poppe, M. Poel, Comparison of silhouette shape descriptors for example-based human pose recovery, in: International Conference on Automatic Face and Gesture Recognition, 2006, pp. 541-546.
26. Kauppinen H., Seppänen T., and Pietikäinen M. An experimental comparison of autoregressive and Fourier-based descriptors in 2d shape classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 17 2 (1995) 201-207
27. Shahabi C., and Safar M. An experimental study of alternative shape-based image retrieval techniques. Multimedia Tools and Applications 32 1 (2007) 29-48
28. B. Scassellati, S. Alexopoulos, M. Flickner, Retrieving images by 2d shape: a comparison of computation methods with human perceptual judgments, in: Storage and Retrieval for Image and Video Databases (SPIE), 1994, pp. 2-14.
29. L. Sigal, A.O. Balan, M.J. Black, Humaneva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion, International Journal on Computer Vision (in press), doi:10.1007/s11263-009-0273-6.
30. Wright J., Yang A.Y., Ganesh A., Sastry S.S., and Ma Y. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31 2 (2009) 210-227
31. Moeslund T.B., and Granum E. A survey of computer vision-based human motion capture. Computer Vision and Image Understanding 81 3 (2001) 231-268
32. Moeslund T.B., Hilton A., and Krüger V. A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104 2-3 (2006) 90-126
33. Poppe R. Vision-based human motion analysis: an overview. Computer Vision and Image Understanding 108 1-2 (2007) 4-18
34. L. Bo, C. Sminchisescu, A. Kanaujia, D.N. Metaxas, Fast algorithms for large scale conditional 3d prediction, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2008.
35. C. Sminchisescu, A. Kanaujia, Z. Li, D.N. Metaxas, Discriminative density propagation for 3d human motion estimation, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2005, pp. 390-397.
36. L. Gond, P. Sayd, T. Chateau, M. Dhome, A 3d shape descriptor for human pose recovery, in: Articulated Motion and Deformable Objects, 2008, pp. 370-379.
37. K. Grauman, G. Shakhnarovich, T. Darrell, Inferring 3d structure with a statistical image-based shape model, in: IEEE International Conference on Computer Vision, 2003, pp. 641-648.
38. Y. Sun, M. Bray, A. Thayananthan, B. Yuan, P. Torr, Regression-based human motion capture from voxel data, in: British Machine Vision Conference, 2006, pp. 641-648.
39. C. Sminchisescu, A. Kanaujia, D.N. Metaxas, Learning joint top-down and bottom-up processes for 3d visual inference, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2006, pp. 1743-1752.
40. Rosales R., and Sclaroff S. Combining generative and discriminative models in a framework for articulated pose estimation. International Journal of Computer Vision 67 3 (2006) 251-276
41. M. Isard, Pampas: real-valued graphical models for computer vision, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2003, pp. 613-620.
42. P.A. Viola, M.J. Jones, Rapid object detection using a boosted cascade of simple features, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2001, pp. 511-518.
43. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2005, pp. 886-893.
44. A. Agarwal, B. Triggs, A local basis representation for estimating human pose from cluttered images, in: Asian Conference on Computer Vision, vol. 1, 2006, pp. 50-59.
45. Lowe D.G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60 2 (2004) 91-110
46. P. Scovanner, S. Ali, M. Shah, A 3-dimensional sift descriptor and its application to action recognition, in: ACM Multimedia, 2007, pp. 357-360.
47. Nayak S., Sarkar S., and Loeding B.L. Distribution-based dimensionality reduction applied to articulated motion recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31 5 (2009) 795-810
48. D. Nistér, H. Stewénius, Scalable recognition with a vocabulary tree, in: CVPR, vol. 2, 2006, pp. 2161-2168.
49. T. Serre, L. Wolf, T. Poggio, Object recognition with features inspired by visual cortex, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2005, pp. 994-1000.
50. A. Agarwal, B. Triggs, Hyperfeatures - multilevel local coding for visual recognition, in: Europeon Conference on Computer Vision, vol. 1, 2006, pp. 30-43.
51. S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2006, pp. 2169-2178.
52. A. Kanaujia, C. Sminchisescu, D.N. Metaxas, Semi-supervised hierarchical models for 3d human pose reconstruction, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.
53. Laptev I. On space-time interest points. International Journal of Computer Vision 64 2-3 (2005) 107-123
54. S.-F. Wong, R. Cipolla, Extracting spatiotemporal interest points using global information, in: IEEE International Conference on Computer Vision, 2007, pp. 1-8.
55. G. Willems, T. Tuytelaars, L.J.V. Gool, An efficient dense and scale-invariant spatio-temporal interest point detector, in: Europeon Conference on Computer Vision, vol. 2, 2008, pp. 650-663.
56. Niebles J.C., Wang H., and 0002 F.-F.L. Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision 79 3 (2008) 299-318
57. M. Bregonzio, S. Gong, T. Xiang, Recognising action as clouds of space-time interest points, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009.
58. Fleuret F., Berclaz J., Lengagne R., and Fua P. Multicamera people tracking with a probabilistic occupancy map. IEEE Transaction on Pattern Analysis and Machine Intelligence 30 2 (2008) 267-282
59. Park S., and Trivedi M.M. Multi-person interaction and activity analysis: a synergistic track- and body-level analysis framework. Machine Vision and Applications 18 3-4 (2007) 151-166
60. Arkin E.M., Chew L.P., Huttenlocher D.P., Kedem K., and Mitchell J.S.B. An efficiently computable metric for comparing polygonal shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 13 3 (1991) 209-216
61. W. Niblack, J. Yin, A pseudo-distance measure for 2d shapes based on turning angle, in: ICIP '95: Proceedings of the 1995 International Conference on Image Processing, vol. 3, IEEE Computer Society, Washington, DC, USA, 1995, p. 3352.
62. Zahn C.T., and Roskies R.Z. Fourier descriptors for plane closed curves. IEEE Transactions on Computers c-21 3 (1972) 269-281
63. R.D. de León, L.E. Sucar, Human silhouette recognition with Fourier descriptors, in: International Conference on Pattern Recognition, 2000, pp. 3713-3716.
64. R. Poppe, M. Poel, Example-based pose estimation in monocular images using compact fourier descriptors. CTIT Technical Report series, TR-CTIT-05-49, Centre for Telematics and Information Technology, University of Twente, Enschede, p. 19, October 2005.
65. Hu M.K. Visual pattern recognition by moment invariants. IRE Transactions on Information Theory IT 8 2 (1962) 179-187
66. Chen C.-C. Improved moment invariants for shape discrimination. Pattern Recognition 26 5 (1993) 683-686
67. Joo J.M. Boundary geometric moments and its application to automatic quality control in the industry. Industrial Data 9 1 (2006) 76-84
68. Gorelick L., Galun M., Sharon E., Basri R., and Brandt A. Shape representation and classification using the poisson equation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 12 (2006) 1991-2005
69. Belongie S., Malik J., and Puzicha J. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 4 (2002) 509-522
70. Mori G., Belongie S.J., and Malik J. Efficient shape matching using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 11 (2005) 1832-1837
71. Huttenlocher D.P., Klanderman G.A., and Rucklidge W. Comparing images using the hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15 9 (1993) 850-863
72. M.P. Dubuisson, A.K. Jain, A modified Hausdorff distance for object matching, in: International Conference on Pattern Recognition, vol. 1, 1994, pp. 566-568, .
73. Bimbo A.D., and Pala P. Visual image retrieval by elastic matching of user sketches. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 2 (1997) 121-132
74. Gdalyahu Y., and Weinshall D. Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes. IEEE Transactions on Pattern Analysis and Machine Intelligence 21 12 (1999) 1312-1328
75. Geiger D., Gupta A., Costa L.A., and Vlontzos J. Dynamic programming for detecting, tracking, and matching deformable contours. IEEE Transactions on Pattern Analysis and Machine Intelligence 17 3 (1995) 294-302
76. Petrakis E.G.M., Diplaros A., and Milios E.E. Matching and retrieval of distorted and occluded shapes using dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 11 (2002) 1501-1516
77. C. Chen, Y. Zhuang, J. Xiao, F. Wu, Adaptive and compact shape descriptor by progressive feature combination and selection with boosting, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2008.
78. A. Bissacco, M.-H. Yang, S. Soatto, Fast human pose estimation using appearance and motion via multi-dimensional boosting regression, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.
79. R. Okada, S. Soatto, Relevant feature selection for human pose estimation and localization in cluttered images, in: Europeon Conference on Computer Vision, vol. 2, 2008, pp. 434-445.
80. G. Rogez, J. Rihan, S. Ramalingam, C. Orrite, P.H.S. Torr, Randomized trees for human pose detection, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2008.