Mid-level features and spatio-temporal context for activity recognition

Pattern Recognition

Volumn 45, Issue 12, 2012, Pages 4182-4191

  Yuan, Fei ^a,b   Xia, Gui Song ^c   Sahbi, Hichem ^b   Prinet, Veronique ^a  

^a INSTITUTE OF AUTOMATION   (China)

^b INSTITUT TELECOM   (France)

^c UNIVERSITÉ PARIS DAUPHINE   (France)

Author keywords

Activity components; Activity recognition; Mid level features; Spatio temporal context kernels

Indexed keywords

ACTIVITY RECOGNITION; BODY PARTS; CONTEXT INFORMATION; CONTEXTUAL INFORMATION; DATA SETS; HUMAN ACTIVITIES; LOCAL PROPERTY; LOW-LEVEL FEATURES; MID-LEVEL FEATURES; SPATIO-TEMPORAL; TEMPORAL DOMAIN;

PATTERN RECOGNITION;

SOFTWARE ENGINEERING;

EID: 84864280769     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.05.001     Document Type: Article

References (42)

1. A. Gilbert, J. Illingworth, R. Bowden, Fast realistic multi-action recognition using mined dense spatio-temporal features, in: Proceedings of the International Conference on Computer Vision (ICCV), 2009.
2. P. Siva, T. Xiang, Action detection in crowd, in: Proceedings of the British Machine Vision Conference (BMVC), 2010.
3. I. Laptev, T. Lindeberg, On space-time interest points, in: Proceedings of the International Conference on Computer Vision (ICCV), 2003.
4. P. Dollar, V. Rabaud, G. Cottrell, S. Belongie, Behavior recognition via sparse spatio-temporal features, in: Proceedings of the International Conference on VS-PETS, 2005.
5. G. Willems, T. Tuytelaars, L.V. Gool, An efficient dense and scale-invariant spatio-temporal interest point detector, in: Proceedings of the European Conference on Computer Vision (ECCV), 2008.
6. C. Schuldt, I. Laptev, B. Caputo, Recognizing human actions: a local SVM approach, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2004.
7. I. Laptev, M. Marszałek, C. Schmid, B. Rozenfeld, Learning realistic human actions from movies, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
8. M. Bregonzio, S. Gong, T. Xiang, Recognising action as clouds of space-time interest points, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
9. A. Kovashka, K. Grauman, Learning a hierarchy of discriminative space-time neighborhood features for human action recognition, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
10. M.S. Ryoo, J.K. Aggarwal, Spatio-temporal relationship match: video structure comparison for recognition of complex human activities, in: Proceedings of the International Conference on Computer Vision (ICCV), 2009.
11. A.F. Bobick, J.W. Davis, I.C. Society, I.C. Society The recognition of human movement using temporal templates IEEE Transactions on Pattern Analysis and Machine Intelligence 23 3 2001 257 267
12. L. Gorelick, M. Blank, E. Shechtman, M. Irani, R. Basri Actions as space-time shapes IEEE Transactions on Pattern Analysis and Machine Intelligence 29 12 2007 2247 2253
13. B. Yao, S.C. Zhu, Learning deformable action templates from cluttered videos, in: Proceedings of the International Conference on Computer Vision (ICCV), 2009.
14. Y. Ke, R. Sukthankar, M. Hebert, Event detection in crowded videos, in: Proceedings of the International Conference on Computer Vision (ICCV), 2007.
15. P.F. Felzenszwalb, D.P. Huttenlocher, Efficient matching of pictorial structures, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2000.
16. Y. Wang, G. Mori, Learning a discriminative hidden part model for human action recognition, in: Advances in Neural Information Processing Systems (NIPS), 2008.
17. J. Niebles, L. Fei-Fei, A hierarchical model of shape and appearance for human action classification, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2007.
18. J. Sun, X. Wu, S. Yan, L. F. Cheong, T.-S. Chua, J. Li, Hierarchical spatio-temporal context modeling for action recognition., in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
19. R. Messing, C. Pal, H. Kautz, Activity recognition using the velocity histories of tracked keypoints, in: Proceedings of the International Conference on Computer Vision (ICCV), 2009.
20. P. Turaga, R. Chellappa, Locally time-invariant models of human activities using trajectories on the Grassmannian, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
21. H. Wang, A. Kläser, C. Schmid, L. Cheng Lin, Action recognition by dense trajectories, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
22. C. Pinhanez, A. Bobick, Human action detection using pnf propagation of temporal constraints, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 1998.
23. J.M. Siskind Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic Journal of Artificial Intelligence Research 15 2001 31 90
24. R. Nevatia, T. Zhao, S. Hongeng, Hierarchical language-based representation of events in video streams, in: Conference on Computer Vision and Pattern Recognition Workshop, 2003. CVPRW 03, 2003.
25. Y. Shi, Y. Huang, D. Minnen, A. Bobick, I. Essa, Propagation networks for recognition of partially ordered sequential action, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2004.
26. M. Ryoo, J. Aggarwal, Recognition of composite human activities through context-free grammar based representation, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2006.
27. A. Gupta, P. Srinivasan, J. Shi, L. Davis, Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
28. J. Wang, Z. Chen, Y. Wu, Action recognition with multiscale spatio-temporal contexts, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
29. B. Yao, L. Fei-Fei, Modeling mutual context of object and human pose in human-object interaction activities, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
30. F. Yuan, J. Yuan, V. Prinet, Middle-level representation for human activities recognition: the role of spatio-temporal relationships, in: ECCV10 Workshop on Human Motion: Understanding, Modeling, Capture and Animation, 2010.
31. D.G. Lowe Distinctive image features from scale-invariant keypoints International Journal of Computer Vision 60 2 2004 91 110
32. K. Mikolajczyk, C. Schmid A performance evaluation of local descriptors IEEE Transactions on Pattern Analysis and Machine Intelligence 27 10 2005 1615 1630
33. S. Rao, R. Tron, R. Vidal, Y. Ma Motion segmentation in the presence of outlying, incomplete, or corrupted trajectories IEEE Transactions on Pattern Analysis and Machine Intelligence 99 10 2009 1832 1845
34. P.F. Felzenszwalb, D.P. Huttenlocher Efficient graph-based image segmentation International Journal of Computer Vision 59 2 2004 167 181
35. J. Shi, J. Malik Normalized cuts and image segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence 22 8 2000 888 905
36. M.S. Ryoo, J.K. Aggarwal, UT-Interaction Dataset, ICPR Contest on Semantic Description of Human Activities (SDHA) 〈 http://cvrc.ece.utexas. edu/SDHA2010/Human-Interaction.html 〉 (2010).
37. S. Belongie, G. Mori, J. Malik, Matching with shape contexts, in: Proceedings of the IEEE Workshop on Content-Based Access of Image and Video-Libraries, 2000.
38. P. Perona, J. Malik Scale-space and edge detection using anisotropic diffusion IEEE Transactions on Pattern Analysis and Machine Intelligence 12 7 1990 629 639
39. C. Rao, A. Yilmaz, M. Shah View-invariant representation and recognition of actions International Journal of Computer Vision 50 2 2002 203 226
40. H. Sahbi, J.-Y. Audibert, J. Rabarisoa, R. Keriven, Context-dependent kernel design for object matching and recognition, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
41. M.S. Ryoo, C.-C. Chen, J.K. Aggarwal, A. Roy-Chowdhury, An Overview of Contest on Semantic Description of Human Activities (SDHA) 2010 〈 http://cvrc.ece.utexas.edu/SDHA2010/Human-Interaction.html 〉.
42. D. Waltisberg, A. Yao, J. Gall, L. Van Gool, Variations of a Hough-Voting action recognition system, in: Proceedings of the International Conference on Pattern Recognition (ICPR) contest on Semantic Description of Human Activities (SDHA), 2010.