Mining 3D Key-Pose-Motifs for Action Recognition

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

Volumn 2016-December, Issue , 2016, Pages 2639-2647

  Wang, Chunyu ^a   Wang, Yizhou ^a   Yuille, Alan L ^b,c  

^a China   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPUTER VISION; PATTERN RECOGNITION;

ACTION RECOGNITION; ACTION SEQUENCES; BENCHMARK DATASETS; KEY POSE; MATCHING SCORE; OVERFITTING; STATE-OF-THE-ART PERFORMANCE;

CLASSIFICATION (OF INFORMATION);

EID: 84986266365     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.289     Document Type: Conference Paper

References (33)

1. R. Agrawal and R. Srikant. Mining sequential patterns. In ICDE, pages 3-14. IEEE, 1995.
2. R. Agrawal, R. Srikant, et al. Fast algorithms for mining association rules. In VLDB, volume 1215, pages 487-499, 1994.
3. J. Ben-Arie, Z. Wang, P. Pandit, and S. Rajaram. Human activity recognition using multidimensional indexing. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(8):1091-1104, 2002.
4. I. Bülthoff, H. Bülthoff, and P. Sinha. Top-down influences on stereoscopic depth-perception. Nature neuroscience, 1(3):254-257, 1998.
5. M. Devanne, H. Wannous, S. Berretti, P. Pala, M. Daoudi, and A. Del Bimbo. 3-d human action recognition by shape analysis of motion trajectories on riemannian manifold. 2014.
6. Y. Du, W. Wang, and L. Wang. Hierarchical recurrent neural network for skeleton based action recognition. In CVPR. IEEE, 2015.
7. A. Efros, A. C. Berg, G. Mori, J. Malik, et al. Recognizing action at a distance. In ICCV, pages 726-733. IEEE, 2003.
8. W. Gong, A. D. Bagdanov, F. X. Roca, and J. Gonzàlez. Automatic key pose selection for 3d human action recognition. In Articulated Motion and Deformable Objects, pages 290-299. Springer, 2010.
9. G. Johansson. Visual perception of biological motion and a model for its analysis. Perception & psychophysics, 14(2):201-211, 1973.
10. I. Laptev. On space-time interest points. International Journal of Computer Vision, 64(2-3):107-123, 2005.
11. W. Li, Z. Zhang, and Z. Liu. Action recognition based on a bag of 3d points. In CVPRW, pages 9-14. IEEE, 2010.
12. J. Luo, W. Wang, and H. Qi. Group sparsity and geometry constrained dictionary learning for action recognition from depth maps. In ICCV, pages 1809-1816. IEEE, 2013.
13. M. Muzammal and R. Raman. Mining sequential patterns from probabilistic databases. In Advances in Knowledge Discovery and Data Mining, pages 210-221. Springer, 2011.
14. O. Oreifej and Z. Liu. Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences. In CVPR, pages 716-723. IEEE, 2013.
15. J. R. Padilla-López, A. A. Chaaraoui, and F. Flórez-Revuelta. A discussion on the validation tests employed to compare human action recognition methods using the msr action3d dataset. arXiv preprint arXiv:1407.7390, 2014.
16. M. Raptis, I. Kokkinos, and S. Soatto. Discovering discriminative action parts from mid-level video representations. In CVPR, pages 1242-1249. IEEE, 2012.
17. L. Seidenari, V. Varano, S. Berretti, A. Del Bimbo, and P. Pala. Recognizing actions from depth cameras as weakly aligned multi-part bag-of-poses. In CVPRW, pages 479-485. IEEE, 2013.
18. S. Sempena, N. U. Maulidevi, and P. R. Aryan. Human action recognition using dynamic time warping. In Electrical Engineering and Informatics (ICEEI), 2011 International Conference on, pages 1-5. IEEE, 2011.
19. J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio, A. Blake, M. Cook, and R. Moore. Real-time human pose recognition in parts from single depth images. Communications of the ACM, 56(1):116-124, 2013.
20. R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. Springer, 1996.
21. Q. D. Tran and N. Q. Ly. Sparse spatio-temporal representation of joint shape-motion cues for human action recognition in depth sequences. In Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2013 IEEE RIVF International Conference on, pages 253-258. IEEE, 2013.
22. N. Vaswani, A. R. Chowdhury, and R. Chellappa. Activity recognition using the dynamics of the configuration of interacting objects. In CVPR, volume 2, pages II-633. IEEE, 2003.
23. R. Vemulapalli, F. Arrate, and R. Chellappa. Human action recognition by representing 3d skeletons as points in a lie group. In CVPR, pages 588-595. IEEE, 2014.
24. C. Wang, J. Flynn, Y. Wang, and A. L. Yuille. Recognizing actions in 3d by action-snippets and activated simplices. AAAI, 2016.
25. C. Wang, Y. Wang, and A. L. Yuille. An approach to posebased action recognition. In CVPR, pages 915-922. IEEE, 2013.
26. H.Wang, A. Klaser, C. Schmid, and C.-L. Liu. Action recognition by dense trajectories. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 3169-3176. IEEE, 2011.
27. J. Wang, Z. Liu, Y. Wu, and J. Yuan. Mining actionlet ensemble for action recognition with depth cameras. In CVPR, pages 1290-1297. IEEE, 2012.
28. D. Weinland, M. Ö zuysal, and P. Fua. Making action recognition robust to occlusions and viewpoint changes. In Computer Vision-ECCV 2010, pages 635-648. Springer, 2010.
29. L. Xia and J. Aggarwal. Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In CVPR, pages 2834-2841. IEEE, 2013.
30. L. Xia, C.-C. Chen, and J. Aggarwal. View invariant human action recognition using histograms of 3d joints. In CVPRW, pages 20-27. IEEE, 2012.
31. W. Yang, Y. Wang, and G. Mori. Recognizing human actions from still images with latent poses. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 2030-2037. IEEE, 2010.
32. M. J. Zaki. Spade: An efficient algorithm for mining frequent sequences. Machine learning, 42(1-2):31-60, 2001.
33. Z. Zhao, D. Yan, and W. Ng. Mining probabilistically frequent sequential patterns in large uncertain databases. Knowledge and Data Engineering, IEEE Transactions on, 26(5):1171-1184, 2014.