Action bank: A high-level representation of activity in video

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

Volumn , Issue , 2012, Pages 1234-1241

  Sadanand, Sreemanananth ^a   Corso, Jason J ^a  

^a SUNY at Buffalo ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVITY RECOGNITION; IMAGE REPRESENTATIONS; LINEAR SVM; MID-LEVEL FEATURES; SEMANTIC SPACE; STATE OF THE ART;

SEMANTICS;

COMPUTER VISION;

EID: 84866718894     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247806     Document Type: Conference Paper

References (39)

1. http://server.cs.ucf.edu/~vision/data.html.
2. S. Ali, A. Basharat, and M. Shah. Chaotic invariants for human action recognition. In ICCV, 2007.
3. A. Bobick and J. Davis. The Recognition of Human Movement Using Temporal Templates. TPAMI, 23(3):257-267, 2001.
4. M. Bregonzio, S. Gong, and T. Xiang. Recognising action as clouds of spacetime interest points. In CVPR, 2009.
5. K. G. Derpanis and J. Gryn. Three-dimensional nth derivative of gaussian separable steerable filters. In IEEE ICIP, 2005.
6. K. G. Derpanis, M. Sizintsev, K. Cannons, and R. P. Wildes. Efficient action spotting based on a spacetime oriented structure representation. In CVPR, 2010.
7. A. A. Efros, A. Berg, G. Mori, and J. Malik. Recognizing action at a distance. In ICCV, 2003.
8. I. Essa and A. Pentland. Coding, analysis, interpretation and recognition of facial expressions. TPAMI, 19(7):757-763, 1997.
9. W. Freeman and E. Adelson. The design and use of steerable filters. TPAMI, 13(9):891-906, 1991.
10. M. A. Giese. Neural model for the recognition of biological motion, volume Dynamische Perzeption, pages 105-110. Infix Verlag, 2000.
11. A. Gilbert, J. Illingworth, and R. Bowden. Fast realistic multi-action recognition using mined dense spatio-temporal features. In CVPR, 2009.
12. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri. Actions as spacetime shapes. TPAMI, 29(12):2247-2253, 2007.
13. A. Hauptmann, R. Yan, W.-H. Lin, M. Christel, and H. Wactlar. Can high-level concepts fill the semantic gap in video retrieval? TMM, 9(5):958-966, 2007.
14. G. Johanssson. Visual-perception of biological motion and a model for its analysis. Perception & Psychophysics, 14(2):201-211, 1973.
15. A. Klaser, M. Marszalek, and C. Schmid. A spatio-temporal descriptor based on 3d-gradients. In BMVC, 2008.
16. A. Kovashka and K. Grauman. Learning a hierarchy of discriminative spacetime neighborhood features for human action recognition. In CVPR, 2010.
17. H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. Hmdb: A large video database for human motion recognition. In ICCV, 2011.
18. I. Laptev. On space-time interest points. IJCV, 64(2/3):107-123, 2005.
19. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR, 2008.
20. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
21. Q. Le, W. Zou, S. Yeung, and A. Ng. Learning hierarchical invariant spatiotemporal features for action recognition with independent subspace analysis. In CVPR, 2011.
22. L.-J. Li, H. Su, E. P. Xing, and L. Fei-Fei. Object bank: A high-level image representation for scene classification and semantic feature sparsification. In NIPS, 2010.
23. J. Liu, B. Kuipers, and S. Savarese. Recognizing human actions by attributes. In CVPR, 2011.
24. J. Liu, J. Luo, and M. Shah. Recognizing realistic actions from videos "in the wild". In CVPR, 2009.
25. J. Liu and M. Shah. Learning human actions via information maximization. In CVPR, 2008.
26. A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 42:145-175, 2001.
27. D. I. Perrett, P. A. Smith, A. J. Mistlin, A. J. Chitty, A. S. Head, D. D. Potter, R. Broennimann, A. D. Milner, and M. A. Jeeves. Visual analysis of body movements by neurones in the temporal cortex of the macaque monkey: a preliminary report. Behavioural Brain Research, 16(2-3):153-170, 1985.
28. R. Polana and R. Nelson. Low level recognition of human motion (or how to your main without funding his body parts). In IEEE MNRAO Workshop, 1994.
29. D. Ramanan and D. A. Forsyth. Automatic annotation of everyday movements. In NIPS, 2003.
30. M. D. Rodriguez, J. Ahmed, and M. Shah. Action mach: A spatio-temporal maximum average correlation height filter for action recognition. In CVPR, 2008.
31. M. S. Ryoo, M. Shah, and J. K. Aggarwal. Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities. In CVPR, 2009.
32. S. Savarese, A. DelPozo, J. C. Niebles, and L. Fei-Fei. Spatial-temporal correlations for unsupervised action classification. In IEEE WMVC, 2008.
33. C. Schüldt, I. Laptev, and B. Caputo. Recognizing human actions: a local SVM approach. In ICPR, 2004.
34. D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. In CVPR, 2010.
35. M. Varma and B. R. Babu. More generality in efficient multiple kernel learning. In ICML, 2009.
36. H. Wang, A. Kläser, C. Schmid, and C.-L. Liu. Action recognition by dense trajectories. In CVPR, 2011.
37. H. Wang, M. Ullah, A. Kläser, I. Laptev, and C. Schmid. Evaluation of local spatio-temporal features for action recognition. In BMVC, 2009.
38. X. Wu, D. Xu, L. Duan, and J. Luo. Action recognition using context and appearance distribution features. In CVPR, 2011.
39. L. Yeffet and L. Wolf. Local trinary patterns for human action recognition. In ICCV, 2009.