Parsing videos of actions with segmental grammars

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

Volumn , Issue , 2014, Pages 612-619

  Pirsiavash, Hamed ^a   Ramanan, Deva ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION; SUPPORT VECTOR MACHINES;

HIERARCHICAL STRUCTURES; HUMAN ACTIVITIES; LINEAR TIME; REAL WORLD VIDEOS; SUB-ACTIONS; TEMPORAL STRUCTURES; YOUTUBE;

TEMPORAL LOGIC;

EID: 84911384466     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.85     Document Type: Conference Paper

References (35)

1. J. K. Aggarwal and M. S. Ryoo. Human activity analysis: A review. ACM Comput. Surv., 43(3):16, 2011.
2. W. Brendel, A. Fern, and S. Todorovic. Probabilistic event logic for interval-based event recognition. In CVPR, 2011.
3. N. Chomsky. Three models for the description of language. Information Theory, IRE Transactions on, 1956.
4. A. Efros, A. Berg, G. Mori, and J. Malik. Recognizing action at a distance. In CVPR, 2003.
5. S. Fine, Y. Singer, and N. Tishby. The hierarchical hidden markov model: Analysis and applications. Machine learning, 32(1):41-62, 1998.
6. M. Hoai, Z. Lan, and F. De la Torre. Joint segmentation and classification of human actions in video. In CVPR, 2011.
7. Y. Ivanov and A. Bobick. Recognition of visual activities and interactions by stochastic parsing. IEEE PAMI, 2000.
8. J. Janssen and N. Limnios. Semi-Markov models and applications. Kluwer Academic Publishers, 1999.
9. T. Joachims, T. Finley, and C. Yu. Cutting plane training of structural SVMs. Machine Learning, 2009.
10. S. Joo and R. Chellappa. Attribute grammar-based event recognition and anomaly detection. In CVPR-W, 2006.
11. Y. Ke, R. Sukthankar, and M. Hebert. Event detection in crowded videos. In ICCV, pages 1-8. IEEE, 2007.
12. S. Kwak, B. Han, and J. Han. On-line video event detection by constraint flow. IEEE PAMI, 2013.
13. I. Laptev and P. Perez. Retrieving actions in movies. In International Conference on Computer Vision, 2007.
14. C. Manning and H. Schütze. Foundations of statistical natural language processing. MIT press, 1999.
15. D. Moore and I. Essa. Recognizing multitasked activities from video using stochastic context-free grammar. In National Conf on Artificial Intelligence, 2002.
16. J. Niebles, C. Chen, and L. Fei-Fei. Modeling temporal structure of decomposable motion segments for activity classification. ECCV, pages 392-405, 2010.
17. N. Oliver, A. Garg, and E. Horvitz. Layered representations for learning and inferring office activity from multiple sensory channels. CVIU, 96(2):163-180, 2004.
18. H. Pirsiavash and D. Ramanan. Detecting activities of daily living in first-person camera views. In CVPR, 2012.
19. R. Polana and R. Nelson. Detection and recognition of periodic, nonrigid motion. IJCV, 23(3):261-282, 1997.
20. R. Poppe. A survey on vision-based human action recognition. Image and Vision Computing, 2010.
21. M. Rodriguez, J. Ahmed, and M. Shah. Action mach a spatio-temporal maximum average correlation height filter for action recognition. In CVPR, pages 1-8, 2008.
22. M. S. Ryoo and J. K. Aggarwal. Semantic representation and recognition of continued and recursive human activities. IJCV, 82(1):1-24, 2009.
23. S. Sarawagi and W. Cohen. Semi-markov conditional random fields for information extraction. NIPS, 2004.
24. E. Shechtman and M. Irani. Space-time behavior based correlation. In IEEE PAMI, 2007.
25. Q. Shi, L. Cheng, L. Wang, and A. Smola. Human action segmentation and recognition using discriminative semi-markov models. IJCV, pages 1-11, 2010.
26. Z. Si, M. Pei, B. Yao, and S. Zhu. Unsupervised learning of event and-or grammar and semantics from video. ICCV, 2011.
27. K. Slonneger and B. L. Kurtz. Formal syntax and semantics of programming languages. Addison-Wesley, 1995.
28. K. Tang, L. Fei-Fei, and D. Koller. Learning latent temporal structure for complex event detection. In CVPR, 2012.
29. S. Tran and L. Davis. Event modeling and recognition using markov logic networks. ECCV, 2008.
30. T. T. Truyen, D. Q. Phung, H. H. Bui, and S. Venkatesh. Hierarchical semi-markov conditional random fields for recursive sequential data. In NIPS, pages 1657-1664, 2008.
31. H. Wang, M. M. Ullah, A. Klaser, I. Laptev, and C. Schmid. Evaluation of local spatio-temporal features for action recognition. In BMVC, 2009.
32. S. Wang, A. Quattoni, L. Morency, D. Demirdjian, and T. Darrell. Hidden conditional random fields for gesture recognition. In CVPR. IEEE, 2006.
33. A. Wilson and A. Bobick. Parametric hidden markov models for gesture recognition. PAMI, 21(9):884-900, 1999.
34. C.-N. J. Yu and T. Joachims. Learning structural svms with latent variables. In ICML, 2009.
35. A. L. Yuille, A. Rangarajan, and A. Yuille. The concave-convex procedure (cccp). NIPS, 2:1033-1040, 2002.