Modelling atomic actions for activity classification

Proceedings - IEEE International Conference on Multimedia and Expo

Volumn , Issue , 2012, Pages 278-283

  Zhang, Jiangen ^a,c   Yao, Benjamin ^b,c   Wang, Yongtian ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c Lotus Hill Research Institute   (China)

Author keywords

Activity classification; atomic action; temporal relation

Indexed keywords

ATOMIC ACTIONS; CO-OCCURRENCE; COMPLEX ACTIVITY; DATA SETS; GENERATIVE MODEL; HIERARCHICAL DIRICHLET PROCESS; INTERMEDIATE RESULTS; LINEAR SVM; MIXTURE MODEL; NAIVE BAYESIAN; OLYMPICS; STATE-OF-ART METHODS; TEMPORAL RELATION; VIDEO SEQUENCES; VISUAL INTEREST POINTS; VISUAL WORD;

EXHIBITIONS;

ATOMS;

EID: 84868102744     PISSN: 19457871     EISSN: 1945788X     Source Type: Conference Proceeding    
DOI: 10.1109/ICME.2012.139     Document Type: Conference Paper

References (18)

1. J. C. Niebles, C.-W. Chen, and L. Fei-Fei, "Modeling temporal structure of decomposable motion segments for activity classification," in ECCV, 2010.
2. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld, "Learning realistic human actions from movies," in CVPR, 2008.
3. J. C. Niebles, H. Wang, and L. Fei-Fei, "Unsupervised learning of human action categories using spatialtemporal words," Int. J. Comput. Vision, vol. 79, pp. 299-318, September 2008.
4. Y. Wang and G. Mori, "Human action recognition by semilatent topic models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, pp. 1762-1774, October 2009.
5. A. Gaidon, Z. Harchaoui, and C. Schmid, "Actom Sequence Models for Efficient Action Detection," in CVPR, 2011.
6. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei, "Hierarchical dirichlet processes," Journal of the American Statistical Association, vol. 101, no. 476, pp. 1566-1581, 2006.
7. E. B. Sudderth, A. Torralba, W. T. Freeman, and A. S. Willsky, "Describing visual scenes using transformed objects and parts," Int. J. Comput. Vision, vol. 77, pp. 291-330, May 2008.
8. D. A. Forsyth, O. Arikan, L. Ikemoto, J. O'Brien, and D. Ramanan, "Computational studies of human motion: part 1, tracking and motion synthesis," Found. Trends. Comput. Graph. Vis., vol. 1, pp. 77-254, 2005.
9. P. Turaga, R. Chellappa, V. S. Subrahmanian, and O. Udrea, "Machine recognition of human activities: A survey," 2008.
10. I. Laptev and T. Lindeberg, "Space-time interest points," in ICCV, 2003, pp. 432-439.
11. B. Yao and S.-C. Zhu, "Learning deformable action templates form cluttered videos," in ICCV, 2009.
12. B. Laxton, J. Lim, and D. Kriegman, "Leveraging temporal, contextual and ordering constraints for recognizing complex activities," in CVPR, 2007.
13. J. Liu, B. Kuipers, and S. Savarese, "Recognizing human actions by attributes," in CVPR, 2011.
14. B. Yao and L. Fei-Fei, "Modeling mutual context of object and human pose in human-object interaction activities," in CVPR, 2010.
15. P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie, "Behavior recognition via sparse spatio-temporal features," in VS-PETS, October 2005.
16. G. Willems, T. Tuytelaars, and L. Gool, "An efficient dense and scale-invariant spatio-temporal interest point detector," in ECCV, 2008.
17. D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," J. Mach. Learn. Res., vol. 3, pp. 993-1022, March 2003.
18. X. Wang, X. Ma, and W. E. L. Grimson, "Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, pp. 539-555, March 2009.