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Applied Computational Intelligence and Soft Computing
Volume 2017, Article ID 5861435, 11 pages
https://doi.org/10.1155/2017/5861435
Research Article

Mining Key Skeleton Poses with Latent SVM for Action Recognition

1School of Computer Engineering and Science, Shanghai University, Shanghai, China
2School of Mathematic and Statistics, Nanyang Normal University, Nanyang, China

Correspondence should be addressed to Xiaoqiang Li; nc.ude.uhs.i@ilqx and Dong Liao; nc.ude.unyn@gnodoail

Received 23 August 2016; Revised 8 November 2016; Accepted 15 December 2016; Published 23 January 2017

Academic Editor: Lei Zhang

Copyright © 2017 Xiaoqiang Li 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. J. Wang, Z. Liu, Y. Wu, and J. Yuan, “Learning actionlet ensemble for 3D human action recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 5, pp. 914–927, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. 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
  3. H. Wang, A. Kläser, C. Schmid, and C.-L. Liu, “Dense trajectories and motion boundary descriptors for action recognition,” International Journal of Computer Vision, vol. 103, no. 1, pp. 60–79, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. A. Klaser, M. Marszalek, and C. Schmid, “A spatio-temporal descriptor based on 3d-gradients,” in Proceedings of the 19th British Machine Vision Conference (BMVC '08), p. 275, British Machine Vision Association, 2008.
  5. K. G. Derpanis, M. Sizintsev, K. J. Cannons, and R. P. Wildes, “Action spotting and recognition based on a spatiotemporal orientation analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 3, pp. 527–540, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Sadanand and J. J. Corso, “Action bank: a high-level representation of activity in video,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 1234–1241, IEEE, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Fathi and G. Mori, “Action recognition by learning mid-level motion features,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, IEEE, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Raptis, I. Kokkinos, and S. Soatto, “Discovering discriminative action parts from mid-level video representations,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), June 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. K. Schindler and L. Van Gool, “Action snippets: how many frames does human action recognition require?” 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
  10. “kinect—australia,” http://www.xbox.com/en-AU/Kinect.
  11. W. Li, Z. Zhang, and Z. Liu, “Action recognition based on a bag of 3D points,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW '10), pp. 9–14, IEEE, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Wang, Z. Liu, J. Chorowski, Z. Chen, and Y. Wu, “Robust 3D action recognition with random occupancy patterns,” in Computer Vision—ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7–13, 2012, Proceedings, Part II, pp. 872–885, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  13. F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy, “Sequence of the most informative joints (SMIJ): a new representation for human skeletal action recognition,” Journal of Visual Communication and Image Representation, vol. 25, no. 1, pp. 24–38, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Müller and T. Röder, “Motion templates for automatic classification and retrieval of motion capture data,” in Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '06), pp. 137–146, Vienna, Austria, September 2006.
  15. L. Seidenari, V. Varano, S. Berretti, A. Del Bimbo, and P. Pala, “Recognizing actions from depth cameras as weakly aligned multi-part bag-of-poses,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW '13), pp. 479–485, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Devanne, H. Wannous, S. Berretti, P. Pala, M. Daoudi, and A. Del Bimbo, “3-D human action recognition by shape analysis of motion trajectories on riemannian manifold,” IEEE Transactions on Cybernetics, vol. 45, no. 7, pp. 1340–1352, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. T. Batabyal, T. Chattopadhyay, and D. P. Mukherjee, “Action recognition using joint coordinates of 3D skeleton data,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '15), pp. 4107–4111, IEEE, Québec, Canada, September 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Du, Y. Fu, and L. Wang, “Representation learning of temporal dynamics for skeleton-based action recognition,” IEEE Transactions on Image Processing, vol. 25, no. 7, pp. 3010–3022, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. B. Mahasseni and S. Todorovic, “Regularizing long short term memory with 3D human-skeleton sequences for action recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '16), pp. 3054–3062, Las Vegas, NV, USA, June 2016. View at Publisher · View at Google Scholar
  20. X. Li and Q. Yao, “Action detection based on latent key frame,” in Biometric Recognition, pp. 659–668, Springer, Berlin, Germany, 2015. View at Google Scholar
  21. W. Zhang, M. Zhu, and K. G. Derpanis, “From actemes to action: a strongly-supervised representation for detailed action understanding,” in Proceedings of the 14th IEEE International Conference on Computer Vision (ICCV '13), Sydney, Australia, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. J. C. Gower, “Generalized procrustes analysis,” Psychometrika, vol. 40, no. 1, pp. 33–51, 1975. View at Google Scholar · View at MathSciNet
  23. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627–1645, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. L. Xia, C.-C. Chen, and J. K. Aggarwal, “View invariant human action recognition using histograms of 3D joints,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW '12), pp. 20–27, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. X. Yang and Y. Tian, “Effective 3D action recognition using EigenJoints,” Journal of Visual Communication and Image Representation, vol. 25, no. 1, pp. 2–11, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. “Florence 3d actions dataset,” http://www.micc.unifi.it/vim/datasets/3dactions/.
  27. C. Wang, Y. Wang, and A. L. Yuille, “An approach to pose-based action recognition,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 915–922, IEEE, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. C. Wang, Y. Wang, and A. L. Yuille, “An approach to pose-based action recognition,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 915–922, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. M. E. Hussein, M. Torki, M. A. Gowayyed, and M. El-Saban, “Human action recognition using a temporal hierarchy of covariance descriptors on 3d joint locations,” in Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI '13), pp. 2466–2472, Beijing, China, August 2013.
  30. L. Gan and F. Chen, “Human action recognition using APJ3D and random forests,” Journal of Software, vol. 8, no. 9, pp. 2238–2245, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. H. Pazhoumand-Dar, C.-P. Lam, and M. Masek, “Joint movement similarities for robust 3D action recognition using skeletal data,” Journal of Visual Communication and Image Representation, vol. 30, article no. 1493, pp. 10–21, 2015. View at Publisher · View at Google Scholar · View at Scopus