Recognizing activities via bag of words for attribute dynamics

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

Volumn , Issue , 2013, Pages 2587-2594

  Li, Weixin ^a   Yu, Qian ^b   Sawhney, Harpreet ^b   Vasconcelos, Nuno ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b SRI INTERNATIONAL   (United States)

Author keywords

activity recognition; attribute; bag of words; dynamics

Indexed keywords

ACTIVITY RECOGNITION; ATTRIBUTE; BAG OF WORDS; EXPERIMENTAL EVALUATION; GENERATIVE MODEL; STATE-OF-THE-ART APPROACH; TEMPORAL STRUCTURES; VIDEO REPRESENTATIONS;

PATTERN RECOGNITION; VIDEO RECORDING;

DYNAMICS;

EID: 84887384267     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.334     Document Type: Conference Paper

References (28)

1. B. Afsari, R. Chaudhry, A. Ravichandran, and R. Vidal. Group action induced distances for averaging and clustering linear dynamical systems with applications to the analysis of dynamic scenes. CVPR, 2012. 2, 5
2. A. Chan and N. Vasconcelos. Classifying video with kernel dynamic textures. CVPR, 2007. 3
3. C. Chang and C. Lin. Libsvm: a library for support vector machines. ACM Trans. Intelligent Systems and Technology, 2(3):27, 2011. 6
4. R. Chaudhry, A. Ravichandran, G. Hager, and R. Vidal. Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions. CVPR, 2009. 1, 2
5. G. Doretto, A. Chiuso, Y. Wu, and S. Soatto. Dynamic textures. IJCV, 51(2):91-109, 2003. 2, 3
6. Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong. Attribute learning for understanding unstructured social activity. ECCV, 2012. 1
7. A. Gaidon, Z. Harchaoui, and C. Schmid. Actom sequence models for efficient action detection. CVPR, 2011. 1, 2
8. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri. Actions as space-time shapes. IEEE TPAMI, 29(12):2247-2253, 2007. 6
9. V. Kellokumpu, G. Zhao, and M. Pietikäinen. Human activity recognition using a dynamic texture based method. BMVC, 2008. 1, 2
10. C. H. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. CVPR, 2009. 2
11. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. CVPR, 2008. 1, 2, 6, 7, 8
12. B. Laxton, J. Lim, and D. Kriegman. Leveraging temporal, contextual and ordering constraints for recognizing complex activities in video. CVPR, 2007. 1, 2
13. B. Li, M. Ayazoglu, T. Mao, O. Camps, and M. Sznaier. Activity recognition using dynamic subspace angles. CVPR, 2011. 2
14. W. Li and N. Vasconcelos. Recognizing activities by attribute dynamics. NIPS, 2012. 1, 2, 3, 4, 6, 7
15. J. Liu, B. Kuipers, and S. Savarese. Recognizing human actions by attributes. CVPR, 2011. 1, 2, 3, 4, 6, 7, 8
16. J. Niebles, C. Chen, and L. Fei-Fei. Modeling temporal structure of decomposable motion segments for activity classification. ECCV, 2010. 1, 2, 7, 8
17. P. Over, G. Awad, J. Fiscus, B. Antonishek, M. Michel, A. F. Smeaton, and W. Kraaij. Trecvid 2011-an overview of the goals, tasks, data, evaluation mechanisms, and metrics. Proceedings of TRECVID 2011, 2011. 7
18. D. Parikh and K. Grauman. Relative attributes. ICCV, 2011. 2
19. N. Rasiwasi, P. J. Moreno, and N. Vasconcelos. Bridging the gap: Query by semantic example. IEEE Trans. Multimedia, 9(5):923-938, 2007. 2
20. N. Rasiwasia and N. Vasconcelos. Holistic context models for visual recognition. IEEE TPAMI, 34(5):902-917, 2012. 2
21. A. Ravichandran, R. Chaudhry, and R. Vidal. Categorizing dynamic textures using a bag of dynamical systems. IEEE TPAMI, (99):1, 2012. 2, 5, 6, 7, 8
22. M. Rohrbach, M. Regneri, M. Andriluka, S. Amin, M. Pinkal, and B. Schiele. Script data for attribute-based recognition of composite activities. ECCV, 2012. 1, 2
23. S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. CVPR, 2012. 1, 2
24. A. I. Schein, L. K. Saul, and L. H. Ungar. A generalized linear model for principal component analysis of binary data. AISTATS, 2003. 3, 5
25. K. Tang, L. Fei-Fei, and D. Koller. Learning latent temporal structure for complex event detection. CVPR, 2012. 1, 7, 8
26. S. Vishwanathan, A. J. Smola, and R. Vidal. Binet-cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes. IJCV, 2006. 6
27. H. Wang, M. Ullah, A. Kläser, I. Laptev, and C. Schmid. Evaluation of local spatio-temporal features for action recognition. BMVC, 2009. 2
28. 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. 2