Robust human action recognition based on spatio-temporal descriptors and motion temporal templates

Optik

Volumn 125, Issue 7, 2014, Pages 1891-1896

  Dou, Jianfang ^a   Li, Jianxun ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

3D SIFT; Action recognition; Generalized Multiple Kernel Learning; Motion history image; Spatio temporal interest points

Indexed keywords

3D SIFT; ACTION RECOGNITION; MOTION HISTORY IMAGES; MULTIPLE KERNEL LEARNING; SPATIO-TEMPORAL INTEREST POINTS;

GESTURE RECOGNITION; IMAGE SEGMENTATION; THREE DIMENSIONAL;

MOTION ESTIMATION;

EID: 84892793983     PISSN: 00304026     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijleo.2013.10.022     Document Type: Article

References (34)

1. D. Weinland, R. Ronfard, and E. Boyer A survey of vision-based methods for action representation, segmentation and recognition Comput. Vis. Image Underst. 115 2 2011 224 241
2. C. Schuldt, I. Laptev, and B. Caputo Recognizing human actions: a local SVM approach Proceedings of the IEEE International Conference on Pattern Recognition Cambridge, England, UK 2004 32 36
3. D.G. Lowe Object recognition from local scale-invariant features Proceedings of the IEEE International Conference on Computer Vision Kerkyra, Corfu, Greece 1999 1150 1157
4. D.G. Lowe Distinctive image features form scale-Invariant keypoints Int. J. Comput. Vis. 60 2 2004 91 110
5. P. Scovanner, S. Ali, and M. Shah A 3-dimensional SIFT descriptor and its application to action recognition Proceedings of the IEEE International Conference on Multimedia ACM Press New York, NY, USA 2007 357 360
6. M. Varma, and D. Ray Learning the discriminative power-invariance trade-off Proceedings of the IEEE International Conference on Computer Vision Rio de Janeiro, Brazil 2007 1 8
7. M. Varma, and B.R. Babu More generality in efficient multiple kernel learning Proceedings of the IEEE Conference on Machine Learning Montreal, Canada 2009 134 141
8. A.F. Bobick, and J.W. Davis The recognition of human movement using temporal templates IEEE Trans. Pattern Anal. Mach. Intell. 23 3 2001 257 267
9. M.K. Hu Visual pattern recognition by moment invariants IRE Trans. Inform. Theory 8 2 1962 179 187
10. S.N. Vitaladevuni, V. Kellokumpu, and L.S. Davis Action recognition using ballistic dynamics Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Anchorage, Alaska, USA 2008 1 8
11. P. Turaga, R. Chellappa, V. Subrahmanian, and O. Udrea Machine recognition of human activities: a survey IEEE Trans. Circuits Syst. Video Technol. 18 11 2008 1473 1488
12. R. Poppe A survey on vision-based human action recognition Image Vis. Comput. 28 6 2010 976 990
13. C. Fanti, L. Zelnik Manor, and P. Perona Hybrid models for human motion recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition San Diego, CA, USA 2005 1166 1173
14. S. Ali, A. Basharat, and M. Shah Chaotic invariants for human action recognition Proceedings of the IEEE International Conference on Computer Vision Rio de Janeiro, Brazil 2007 1 8
15. A. Yilmaz Recognizing human actions in videos acquired by uncalibrated moving cameras Proceedings of the IEEE International Conference on Computer Vision Beijing, China 2005 150 157
16. A. Yilmaz, and M. Shah A differential geometric approach to representing the human actions Comput. Vis. Image Underst. 109 3 2008 335 351
17. Z. Zhang, Y. Hu, S. Chan, and L. Chia Motion context: a new representation for human action recognition Proceedings of the IEEE European Conference on Computer Vision Marseille, France 2008 817 829
18. Y. Hu, L. Cao, F. Lv, S. Yan, Y. Gong, and T. Huang Action detection in complex scenes with spatial and temporal ambiguities Proceedings of the IEEE International Conference on Computer Vision Kyoto, Japan 2009 128 135
19. T. Kobayashi, and N. Otsu Three-way auto-correlation approach to motion recognition Pattern Recogn. Lett. 30 3 2009 212 221
20. I. Laptev On space-time interest points Int. J. Comput. Vis. 64 2 2005 107 123
21. P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie Behavior recognition via sparse spatio-temporal features Proceedings of the Second Joint IEEE International Workshop Visual Surveillance and Performance Evaluation of Tracking and Surveillance 2005 65 72
22. A. Oikonomopoulos, I. Patras, and M. Pantic Spatiotemporal salient points for visual recognition of human actions IEEE Trans. Syst. Man Cybern. B: Cybern. 36 3 2005 710 719
23. Y. Ke, R. Sukthankar, and M. Hebert Efficient visual event detection using volumetric features Proceedings of the IEEE International Conference on Computer Vision Beijing, China 2005 166 173
24. J. Niebles, H. Wang, and L. Fei-Fei Unsupervised learning of human action categories using spatial-temporal words Int. J. Comput. Vis. 79 3 2008 299 318
25. S. Nowozin, G. Bakir, and K. Tsuda Discriminative subsequence mining for action classification Proceedings of the IEEE International Conference on Computer Vision Rio de Janeiro, Brazil 2007 1 8
26. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld Learning realistic human actions from movies Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Anchorage, Alaska, USA 2008 1 8
27. J. Liu, and M. Shah Learning human actions via information maximization Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Anchorage, Alaska, USA 2008 1 8
28. A. Klaser, M. Marszałek, and C. Schmid A spatio-temporal descriptor based on 3D-gradients Proceedings of the British Machine Vision Conference Leeds, UK 2008 995 1004
29. J. Liu, S. Ali, and M. Shah Recognizing human actions using multiple features Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Anchorage, Alaska, USA 2008 1 8
30. K. Jia, and D.-Y. Yeung Human action recognition using local spatio-temporal discriminant embedding Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Anchorage, Alaska, USA 2008 1 8
31. C. Stauffer, and W.E.L. Grimson Learning patterns of activity using real-time tracking IEEE Trans. Pattern Anal. Mach. Intell. 22 8 2000 747 757
32. C. Stauffer, and W.E.L. Grimson Adaptive background mixture models for real-time tracking Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Ft. Collins, CO, USA 1999 246 252
33. C.-C. Chang, and C.-J. Lin LIBSVM: a library for support vector machines ACM Trans. Inform. Syst. Secur. 2 27 2011 1 27
34. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri Actions as space-time shapes IEEE Trans. Pattern Anal. Mach. Intell. 29 12 2007 2247 2253