Recognizing actions in 3D using action-snippets and activated simplices

30th AAAI Conference on Artificial Intelligence, AAAI 2016

Volumn , Issue , 2016, Pages 3604-3610

  Wang, Chunyu ^a   Flynn, John ^b   Wang, Yizhou ^a   Yuille, Alan L ^b  

^a Peking University   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GESTURE RECOGNITION;

ACTION RECOGNITION; ART METHODS; OVERFITTING; SHORT SEQUENCES; TEMPORAL RELATIONSHIPS; TRAINING DATA;

CLASSIFICATION (OF INFORMATION);

EID: 84986264590     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. Arthur, D., and Vassilvitskii, S. 2007. k-means++: The advantages of careful seeding. Proceedings of the eighteenth annual ACM- . . . .
2. Bengio, S.; Pereira, F.; Singer, Y.; and Strelow, D. 2009. Group sparse coding. In NIPS, 82-89.
3. Canas, G. D.; Poggio, T. A.; and Rosasco, L. 2012. Learning Manifolds with K-Means and K-Flats. NIPS.
4. Cutler, A., and Breiman, L. 1994. Archetypal analysis. Technometrics 36(4):338-347.
5. Devanne, M.;Wannous, H.; Berretti, S.; Pala, P.; Daoudi, M.; and Del Bimbo, A. 2015. 3-d human action recognition by shape analysis of motion trajectories on riemannian manifold. Cybernetics, IEEE Transactions on 45(7):1340-1352.
6. Elhamifar, E., and Vidal, R. 2013. Sparse subspace clustering: Algorithm, theory, and applications. PAMI 35(11):2765- 2781.
7. Gu, S.; Zhang, L.; Zuo, W.; and Feng, X. 2014. Projective dictionary pair learning for pattern classification. In NIPS, 793-801.
8. Huggins, P. S. 2005. Sparse Coding via Geometry. Ph.D. Dissertation, Yale University.
9. Ionescu, C.; Papava, D.; Olaru, V.; and Sminchisescu, C. 2014. Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. PAMI 36(7):1325-1339.
10. Kyrillidis, A. T.; Becker, S.; and Cevher, V. 2012. Sparse projections onto the simplex. CoRR abs/1206.1529.
11. Laptev, I. 2005. On space-Time interest points. IJCV.
12. Li, W.; Zhang, Z.; and Liu, Z. 2010. Action recognition based on a bag of 3d points. In (CVPRW), 2010 IEEE Conference on, 9-14. IEEE.
13. Luo, J.; Wang, W.; and Qi, H. 2014. Group sparsity and geometry constrained dictionary learning for action recognition from depth maps. In CVPR. IEEE.
14. Mairal, J.; Bach, F.; Ponce, J.; and Sapiro, G. 2009. Online dictionary learning for sparse coding. ICML 689-696.
15. Oreifej, O., and Liu, Z. 2013. Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences. In CVPR, 716-723. IEEE.
16. Osborne, M. R.; Presnell, B.; and Turlach, B. A. 2000. On the LASSO and its dual. Journal of Computational and Graphical Statistics 9(2):319-337.
17. Padilla-Ĺopez, J. R.; Chaaraoui, A. A.; and Fĺorez-Revuelta, F. 2014. A discussion on the validation tests employed to compare human action recognition methods using the msr action3d dataset. arXiv preprint arXiv:1407.7390.
18. Pitelis, N.; Russell, C.; and Agapito, L. 2013. Learning a Manifold as an Atlas. In CVPR, 1642-1649.
19. Rahmani, H.; Mahmood, A.; Huynh, D. Q.; and Mian, A. 2014. Real time action recognition using histograms of depth gradients and random decision forests. In WACV, 626-633. IEEE.
20. Rubinstein, R., and Elad, M. 2014. Dictionary learning for analysis-synthesis thresholding. Signal Processing, IEEE Transactions on 62(22):5962-5972.
21. Schindler, K., and Van Gool, L. 2008. Action snippets: How many frames does human action recognition require? In CVPR, 1-8. IEEE.
22. Schuldt, C.; Laptev, I.; and Caputo, B. 2004. Recognizing human actions: A local SVM approach. In ICPR, volume 3, 32-36. IEEE.
23. Seidenari, L.; Varano, V.; Berretti, S.; Del Bimbo, A.; and Pala, P. 2013. Recognizing actions from depth cameras as weakly aligned multi-part bag-of-poses. In CVPRW, 479- 485. IEEE.
24. Shotton, J.; Sharp, T.; Kipman, A.; Fitzgibbon, A.; Finocchio, M.; Blake, A.; Cook, M.; and Moore, R. 2013. Realtime human pose recognition in parts from single depth images. Communications of the ACM 56(1):116-124.
25. Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B. Methodological 267-288.
26. Tran, Q. D., and Ly, N. Q. 2013. 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, 253-258. IEEE.
27. Vemulapalli, R.; Arrate, F.; and Chellappa, R. 2014. Human action recognition by representing 3d skeletons as points in a lie group. In CVPR, IEEE Conference on, 588-595. IEEE.
28. Wang, F.; Lee, N.; Sun, J.; Hu, J.; and Ebadollahi, S. 2011. Automatic group sparse coding. In AAAI.
29. Wang, J.; Liu, Z.; Chorowski, J.; Chen, Z.; and Wu, Y. 2012a. Robust 3d action recognition with random occupancy patterns. In ECCV. Springer. 872-885.
30. Wang, J.; Liu, Z.; Wu, Y.; and Yuan, J. 2012b. Mining actionlet ensemble for action recognition with depth cameras. In CVPR, IEEE Conference on, 1290-1297. IEEE.
31. Wang, Y.; Wang, B.; Yu, Y.; Dai, Q.; and Tu, Z. 2015. Action-gons: Action recognition with a discriminative dictionary of structured elements with varying granularity. In ACCV. Springer. 259-274.
32. Wang, C.; Wang, Y.; and Yuille, A. L. 2013. An Approach to Pose-Based Action Recognition. In CVPR, 915-922.
33. Xia, L.; Chen, C.-C.; and Aggarwal, J. 2012. View invariant human action recognition using histograms of 3d joints. In CVPR, 20-27. IEEE.
34. Ziegler, G. M. 2012. Lectures on Polytopes. Springer Science & Business Media.