Mining motion atoms and phrases for complex action recognition

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 2680-2687

  Wang, Limin ^a,b   Qiao, Yu ^b   Tang, Xiaoou ^a,b  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

Author keywords

action recognition; mid level representation

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; MOTION ESTIMATION;

ACTION RECOGNITION; CLASSIFICATION PERFORMANCE; CONSTRUCTION ALGORITHMS; DISCRIMINATIVE ABILITY; DISCRIMINATIVE CLUSTERING; MID-LEVEL REPRESENTATION; MOTION INFORMATION; TEMPORAL CONSTRAINTS;

ATOMS;

EID: 84892618164     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.333     Document Type: Conference Paper

References (30)

1. J. K. Aggarwal and M. S. Ryoo. Human activity analysis: A review. ACM Comput. Surv., 43(3):16, 2011.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB, 1994.
3. M. R. Amer, D. Xie, M. Zhao, S. Todorovic, and S. C. Zhu. Cost-sensitive top-down/bottom-up inference for multiscale activity recognition. In ECCV, pages 187-200, 2012.
4. T. L. Berg, A. C. Berg, and J. Shih. Automatic attribute discovery and characterization from noisy web data. In ECCV, 2010.
5. C.-C. Chang and C.-J. Lin. Libsvm: A library for support vector machines. ACM TIST, 2(3):27, 2011.
6. P. F. Felzenszwalb, R. B. Girshick, D. A. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. TPAMI, 32(9):1627-1645, 2010.
7. B. J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315:972-976, 2007.
8. A. Gaidon, Z. Harchaoui, and C. Schmid. Actom sequence models for efficient action detection. In CVPR, 2011.
9. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri. Actions as space-time shapes. TPAMI, 29(12):2247-2253, 2007.
10. C. Gu, P. A. Arbeĺaez, Y. Lin, K. Yu, and J. Malik. Multi-component models for object detection. In ECCV, 2012.
11. O. Kliper-Gross, Y. Gurovich, T. Hassner, and L. Wolf. Motion interchange patterns for action recognition in unconstrained videos. In ECCV, 2012.
12. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR, 2008.
13. B. Laxton, J. Lim, and D. J. Kriegman. Leveraging temporal, contextual and ordering constraints for recognizing complex activities in video. In CVPR, 2007.
14. J. Liu, B. Kuipers, and S. Savarese. Recognizing human actions by attributes. In CVPR, 2011.
15. J. C. Niebles, C.-W. Chen, and F.-F. Li. Modeling temporal structure of decomposable motion segments for activity classification. In ECCV, 2010.
16. N. Oliver, B. Rosario, and A. Pentland. A bayesian computer vision system for modeling human interactions. TPAMI, 2000.
17. X. Peng, Y. Qiao, Q. Peng, and X. Qi. Exploring motion boundary based sampling and spatial-temporal context descriptors for action recognition. In BMVC, 2013.
18. K. K. Reddy and M. Shah. Recognizing 50 human action categories of web videos. MVAP, 2012.
19. M. Rohrbach, M. Regneri, M. Andriluka, S. Amin, M. Pinkal, and B. Schiele. Script data for attribute-based recognition of composite activities. In ECCV, 2012.
20. S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. In CVPR, 2012.
21. C. Scḧuldt, I. Laptev, and B. Caputo. Recognizing human actions: A local svm approach. In ICPR, 2004.
22. S. Singh, A. Gupta, and A. A. Efros. Unsupervised discovery of mid-level discriminative patches. In ECCV, 2012.
23. K. Tang, F.-F. Li, and D. Koller. Learning latent temporal structure for complex event detection. In CVPR, 2012.
24. H.Wang, A. Kl̈aser, C. Schmid, and C.-L. Liu. Dense trajectories and motion boundary descriptors for action recognition. International Journal of Computer Vision, 103(1):60-79, 2013.
25. L. Wang, Y. Qiao, and X. Tang. Motionlets: Mid-level 3d parts for human motion recognition. In CVPR, pages 2674-2681, 2013.
26. L. Wang, Y. Qiao, and X. Tang. Latent hierarchical model of temporal structure for complex activity classification. TIP, to appear.
27. S. B. Wang, A. Quattoni, L.-P. Morency, D. Demirdjian, and T. Darrell. Hidden conditional random fields for gesture recognition. In CVPR, 2006.
28. X. Wang, L. Wang, and Y. Qiao. A comparative study of encoding, pooling and normalization methods for action recognition. In ACCV, pages 572-585, 2012.
29. B. Yao and F.-F. Li. Grouplet: A structured image representation for recognizing human and object interactions. In CVPR, 2010.
30. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: A comprehensive study. IJCV, 73(2):213-238, 2007.