Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2013, Article ID 405645, 11 pages
http://dx.doi.org/10.1155/2013/405645
Research Article

Multi-Scale Locality-Constrained Spatiotemporal Coding for Local Feature Based Human Action Recognition

College of Information System and Manage, National University of Defense Technology, 109 Deya Road, Changsha, Hunan 410073, China

Received 2 July 2013; Accepted 21 August 2013

Academic Editors: R. Haber, P. Melin, and Y. Zhu

Copyright © 2013 Bin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Y. Wang, Y. Qi, and Y. Li, “Memory-based multiagent coevolution modeling for robust moving object tracking,” The Scientific World Journal, vol. 2013, Article ID 793013, 13 pages, 2013. View at Publisher · View at Google Scholar
  2. T. H. Thi, L. Cheng, J. Zhang, L. Wang, and S. Satoh, “Structured learning of local features for human action classification and localization,” Image and Vision Computing, vol. 30, no. 1, pp. 1–14, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Baek and B.-J. Yun, “A sequence-action recognition applying state machine for user interface,” IEEE Transactions on Consumer Electronics, vol. 54, no. 2, pp. 719–726, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. G. Zhu, M. Yang, K. Yu, W. Xu, and Y. Gong, “Detecting video events based on action recognition in complex scenes using spatio-temporal descriptor,” in Proceedings of the 17th ACM International Conference on Multimedia (MM '09), pp. 165–174, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Weinland, R. Ronfard, and E. Boyer, “A survey of vision-based methods for action representation, segmentation and recognition,” Computer Vision and Image Understanding, vol. 115, no. 2, pp. 224–241, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. J. K. Aggarwal and M. S. Ryoo, “Human activity analysis: a review,” ACM Computing Surveys, vol. 43, no. 3, article 16, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. X. Wu and J. Lai, “Tensor-based projection using ridge regression and its application to action classification,” IET Image Processing, vol. 4, no. 6, pp. 486–493, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. A. A. Chaaraoui and P. Climent-Pérez, “Silhouette-based Human action recognition using sequences of key poses,” Pattern Recognition Letters, vol. 34, no. 15, pp. 1799–1807, 2013. View at Publisher · View at Google Scholar
  9. K. N. Tran, I. A. Kakadiaris, and S. K. Shah, “Modeling motion of body parts for action recognition,” in Proceedings of the British Machine Vision Conference (BMVC '11), pp. 1–12, 2011.
  10. B. Huang, G. Tian, and F. Zhou, “Human typical action recognition using gray scale image of silhouette sequence,” Computers & Electrical Engineering, vol. 38, no. 5, pp. 1177–1185, 2012. View at Google Scholar
  11. S. A. Rahman, M. K. H. Leung, and S.-Y. Cho, “Human action recognition employing negative space features,” Journal of Visual Communication and Image Representation, vol. 24, no. 3, pp. 217–231, 2013. View at Google Scholar
  12. B. Saghafi and D. Rajan, “Human action recognition using Pose-based discriminant embedding,” Signal Processing, vol. 27, no. 1, pp. 96–111, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. S. M. Yoon and A. Kuijper, “Human action recognition based on skeleton splitting,” Expert Systems with Applications, vol. 40, no. 17, pp. 6848–6855, 2013. View at Publisher · View at Google Scholar
  14. L. Shao, L. Ji, Y. Liu, and J. Zhang, “Human action segmentation and recognition via motion and shape analysis,” Pattern Recognition Letters, vol. 33, no. 4, pp. 438–445, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. X. Deng, X. Liu, and M. Song, “LF-EME: local features with elastic manifold embedding for human action recognition,” Neurocomputing, vol. 99, no. 1, pp. 144–153, 2013. View at Google Scholar
  16. P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie, “Behavior recognition via sparse spatio-temporal features,” in Proceedings of the 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS '05), pp. 65–72, October 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Klaser, M. Marszalek, and C. Schmid, “A spatio-temporal descriptor based on 3D-gradients,” Proceedings of the British Machine Vision Conference (BMVC '08), 2008.
  18. P. Scovanner, S. Ali, and M. Shah, “A 3-dimensional sift descriptor and its application to action recognition,” in Proceedings of the 15th ACM International Conference on Multimedia (MM '07), pp. 357–360, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. G. Willems, T. Tuytelaars, and L. Van Gool, “An efficient dense and scale-invariant spatio-temporal interest point detector,” in Proceedings of the Europen Conference on Computer Vision (ECCV '08), pp. 650–663, 2008.
  20. I. Laptev, M. Marszałek, C. Schmid, and B. Rozenfeld, “Learning realistic human actions from movies,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. M.-J. Escobar and P. Kornprobst, “Action recognition via bio-inspired features: the richness of center-surround interaction,” Computer Vision and Image Understanding, vol. 116, no. 5, pp. 593–605, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. X. Zhu, Z. Yang, and J. Tsien, “Statistics of natural action structures and human action recognition,” Journal of Vision, vol. 12, no. 9, pp. 834–834, 2012. View at Google Scholar
  23. B. Chakraborty, M. B. Holte, T. B. Moeslund, and J. Gonzàlez, “Selective spatio-temporal interest points,” Computer Vision and Image Understanding, vol. 116, no. 3, pp. 396–410, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Zhu, X. Zhao, Y. Fu et al., “Sparse coding on local spatial-temporal volumes for human action recognition,” in Proceedings of the Computer Vision (ACCV '10), pp. 660–671, Springer, Berlin, Germany, 2010.
  25. T. Guha and R. K. Ward, “Learning sparse representations for human action recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 8, pp. 1576–1588, 2012. View at Google Scholar
  26. J. C. Van Gemert, C. J. Veenman, A. W. M. Smeulders, and J.-M. Geusebroek, “Visual word ambiguity,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 7, pp. 1271–1283, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature, vol. 381, no. 6583, pp. 607–609, 1996. View at Publisher · View at Google Scholar · View at Scopus
  28. K. Yu, T. Zhang, and Y. Gong, “Nonlinear learning using local coordinate coding,” in Proceedings of the 23rd Annual Conference on Neural Information Processing Systems (NIPS '09), pp. 2223–2231, December 2009. View at Scopus
  29. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 3360–3367, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science, vol. 290, no. 5500, pp. 2323–2326, 2000. View at Publisher · View at Google Scholar · View at Scopus
  31. J. Wang, “Locally linear embedding,” in Geometric Structure of High-Dimensional Data and Dimensionality Reduction, pp. 203–220, Springer, Berlin, Germany, 2011. View at Google Scholar
  32. C. P. Wei, Y. W. Chao, and Y. R. Yeh, “Locality-sensitive dictionary learning for sparse representation based classification,” Pattern Recognition, vol. 46, no. 5, pp. 1277–1287, 2013. View at Google Scholar
  33. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. X. Wu, D. Xu, L. Duan, and J. Luo, “Action recognition using context and appearance distribution features,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 489–496, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. Z. Zhang, C. Wang, B. Xiao et al., “Action recognition using context-constrained linear coding,” Signal Processing Letters, vol. 19, no. 7, pp. 439–442, 2012. View at Google Scholar
  36. M. Bregonzio, T. Xiang, and S. Gong, “Fusing appearance and distribution information of interest points for action recognition,” Pattern Recognition, vol. 45, no. 3, pp. 1220–1234, 2012. View at Publisher · View at Google Scholar · View at Scopus
  37. S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: spatial pyramid matching for recognizing natural scene categories,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '06), pp. 2169–2178, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  38. D. Xu, Y. Huang, Z. Zeng, and X. Xu, “Human gait recognition using patch distribution feature and locality-constrained group sparse representation,” IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 316–326, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. M. Liu, S. Yan, Y. Fu, and T. S. Huang, “Flexible X-Y patches for face recognition,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '08), pp. 2113–2116, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. 2013, http://spams-devel.gforge.inria.fr/.