Action recognition based on local descriptor with dynamic distribution

Journal of Information and Computational Science

Volumn 9, Issue 9, 2012, Pages 2527-2536

  Wang, Yangyang ^a   Li, Yibo ^b   Liu, Yang ^b   Ji, Xiaofei ^b  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^b SHENYANG AEROSPACE UNIVERSITY   (China)

Author keywords

Action recognition; Feature dynamic distribution; Interest points; Local sub regions with salient variations

Indexed keywords

ACTION RECOGNITION; DATA SETS; DESCRIPTORS; DYNAMIC DISTRIBUTION; DYNAMIC VARIATIONS; FLOW FEATURES; GLOBAL FEATURE; GLOBAL LEVELS; INTEREST POINTS; LOCAL DESCRIPTORS; NEAREST NEIGHBOR CLASSIFIERS; SPATIAL TEMPORALS; SPATIOTEMPORAL DISTRIBUTIONS; SUB-REGIONS; VIDEO SEQUENCES;

ALGORITHMS;

MATHEMATICAL TECHNIQUES;

EID: 84865832252     PISSN: 15487741     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (26)

1. X. Ji, H. Liu, Advances in view-invariant human motion analysis: A review, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40 (2010), 13-24
2. R. Poppe, A survey on vision-based human action recognition, Image and Vision Computing, 28 (2010), 976-990
3. T. B. Moeslund, A. Hilton, V. Kruger, A survey of advances in vision-based human motion capture and analysis, Computer Vision and Image Understanding, 104 (2006), 90-126
4. W. Hu, T. Tan, L. Wang et al., A survey on visual surveillance of object motion and behaviors, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 34 (2004), 334-352
5. J. W. Davis, A. F. Bobick, The representation and recognition of human movement using temporal templates, in: IEEE Conference on Computer Vision and Pattern Recognition'97, 1997, 928-934
6. D. Weinland, R. Ronfard, E. Boyer, Free viewpoint action recognition using motion history volumes, Computer Vision and Image Understanding, 104 (2006), 249-257
7. A. Yilmaz, M. Shah, Actions sketch: A novel action representation, in: IEEE Conference on Computer Vision and Pattern Recognition'05, 2005, 984-989
8. I. Laptev, T. Lindeberg, Space-time interest points, in: IEEE 9th International Conference on Computer Vision'03, 2003, 432-439
9. A. Kovashka, K. Grauman, Learning a hierarchy of discriminative space-time neighborhood features for human action recognition, in: IEEE Conference on Computer Vision and Pattern Recognition'10, 2010, 2046-2053
10. K. Prabhakar, S. Oh, P. Wang et al., Temporal causality for the analysis of visual events, in: IEEE Conference on Computer Vision and Pattern Recognition'10, 2010, 1967-1974
11. P. Dollar, V. Rabaud, G. Cottrell et al., Behavior recognition via sparse spatio-temporal features, in: 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance'05, 2005, 65-72
12. I. Laptev, M. Marszalek, C. Schmid et al., Learning realistic human actions from movies, in: IEEE Conference on Computer Vision and Pattern Recognition'08, 2008, 1-8
13. A. Klaser, M. Marszalek, C. Schmid, A spatio-temporal descriptor based on 3D-gradients, in: British Machine Vision Conference'08, 2008, 995-1004
14. G. Willems, T. Tuytelaars, L. Van Gool, An efficient dense and scale-invariant spatio-temporal interest point detector, in: European Conference on Computer Vision'08, 2008, 650-663
15. Z. Gao, M. Chen, A. Hauptmann et al., Comparing evaluation protocols on the KTH dataset, Human Behavior Understanding, 2010, 88-100
16. H. Wang, M. M. Ullah, A. Kläser et al., Evaluation of local spatio-temporal features for action recognition, in: British Machine Vision Conference'09, 2009, 127-137
17. X. Sun, M. Chen, A. Hauptmann, Action recognition via local descriptors and holistic features, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops'09, 2009, 58-65
18. S. Liu, J. Liu, T. Zhang et al., Human action recognition in videos using hybrid motion features, Advances in Multimedia Modeling, 2010, 411-421
19. A. Gilbert, J. Illingworth, R. Bowden, Fast realistic multi-action recognition using mined dense spatio-temporal features, in: IEEE 12th International Conference on Computer Vision'09, 2009, 925-931
20. J. Choi, W. J. Jeon, S. C. Lee, Spatio-temporal pyramid matching for sports videos, in: 1st ACM International Conference on Multimedia Information Retrieval'08, 2008, 291-297
21. L. Gorelick, M. Blank, E. Shechtman et al., Actions as space-time shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (2007), 2247-2253
22. B. K. P. Horn, B. G. Schunck, Determining optical flow, Artificial Intelligence, 17 (1981), 185-203
23. M. Bregonzio, S. Gong, T. Xiang, Recognising action as clouds of space-time interest points, in: IEEE Conference on Computer Vision and Pattern Recognition'09, 2009, 1948-1955
24. M. Blank, L. Gorelick, E. Shechtman et al., Actions as space-time shapes, in: IEEE 10th International Conference on Computer Vision'05, 2005, 1395-1402
25. P. Scovanner, S. Ali, M. Shah, A 3-dimensional sift descriptor and its application to action recognition, in: 15th International Conference on Multimedia'07, 2007, 357-360
26. Z. Lin, Z. Jiang, L. S. Davis, Recognizing actions by shape-motion prototype trees,in: IEEE 12th International Conference on Computer Vision'09, 2009, 444-451