Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 495071, 10 pages
http://dx.doi.org/10.1155/2014/495071
Research Article

The Approach for Action Recognition Based on the Reconstructed Phase Spaces

School of Information Science and Engineering, Central South University, Hunan 410075, China

Received 1 July 2014; Accepted 11 September 2014; Published 10 November 2014

Academic Editor: Shifei Ding

Copyright © 2014 Hong-bin Tu and Li-min Xia. 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. L. Tan, L. Xia, J. Huang, and S. Xia, “Human action recognition based on pLSA model,” Journal of National University of Defense Technology, vol. 35, no. 5, pp. 102–108, 2013. View at Google Scholar · View at Scopus
  2. H.-B. Tu, L.-M. Xia, and L.-Z. Tan, “Adaptive self-occlusion behavior recognition based on pLSA,” Journal of Applied Mathematics, vol. 2013, Article ID 506752, 9 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. H.-B. Tu, L.-M. Xia, and Z.-W. Wang, “The complex action recognition via the correlated topic model,” The Scientific World Journal, vol. 2014, Article ID 810185, 10 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. J. K. Aggarwal and M. S. Ryoo, “Human activity analysis: a review,” ACM Computing Surveys, vol. 43, no. 3, pp. 1–42, 2011. View at Google Scholar
  5. Y. Sheikh, M. Sheikh, and M. Shah, “Exploring the space of a human action,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), pp. 144–149, Beijing, China, October 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Yilmaz and M. Shah, “Recognizing human actions in videos acquired by uncalibrated moving cameras,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), vol. 1, pp. 150–157, October 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. N. Anjum and A. Cavallaro, “Multifeature object trajectory clustering for video analysis,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 11, pp. 1555–1564, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. C. R. Jung, L. Hennemann, and S. R. Musse, “Event detection using trajectory clustering and 4-D histograms,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 11, pp. 1565–1575, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Hervieu, P. Bouthemy, and J.-P. Le Cadre, “A statistical video content recognition method using invariant features on object trajectories,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 11, pp. 1533–1543, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Wang, K. T. Ma, G.-W. Ng, and W. E. L. Grimson, “Trajectory analysis and semantic region modeling using a nonparametric bayesian model,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, Anchorage, Alaska, USA, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. 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
  12. J. Yu, M. Jeon, and W. Pedrycz, “Weighted feature trajectories and concatenated bag-of-features for action recognition,” Neurocomputing, vol. 131, pp. 200–207, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. H.-K. Pao, J. Fadlil, H.-Y. Lin, and K.-T. Chen, “Trajectory analysis for user verification and recognition,” Knowledge-Based Systems, vol. 34, pp. 81–90, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Yi and Y. Lin, “Human action recognition with salient trajectories,” Signal Processing, vol. 93, no. 11, pp. 2932–2941, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. J.-X. Du, K. Yang, and C.-M. Zhai, “Action recognition based on the feature trajectories,” in Intelligent Computing Theories and Applications, vol. 7390 of Lecture Notes in Computer Science, pp. 250–257, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  16. A. Psarrou, S. Gong, and M. Walter, “Recognition of human gestures and behaviour based on motion trajectories,” Image and Vision Computing, vol. 20, no. 5-6, pp. 349–358, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. N.-G. Cho, A. L. Yuille, and S.-W. Lee, “Adaptive occlusion state estimation for human pose tracking under self-occlusions,” Pattern Recognition, vol. 46, no. 3, pp. 649–661, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. V. Maroulas and P. Stinis, “Improved particle filters for multi-target tracking,” Journal of Computational Physics, vol. 231, no. 2, pp. 602–611, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  19. T. Penne, C. Tilmant, T. Chateau, and V. Barra, “Markov chain monte carlo modular ensemble tracking,” Image and Vision Computing, vol. 31, no. 6-7, pp. 434–447, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. M. K. Pitt, R. dos Santos Silva, P. Giordani, and R. Kohn, “On some properties of Markov chain Monte Carlo simulation methods based on the particle filter,” Journal of Econometrics, vol. 171, no. 2, pp. 134–151, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, “Actions as space-time shapes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2247–2253, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. S. de Martino, M. Falanga, and C. Godano, “Dynamical similarity of explosions at Stromboli volcano,” Geophysical Journal International, vol. 157, no. 3, pp. 1247–1254, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. G. Paladin and A. Vulpiani, “Anomalous scaling laws in multifractal objects,” Physics Reports, vol. 156, no. 4, pp. 147–225, 1987. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. S.-C. Fang and H.-L. Chan, “Human identification by quantifying similarity and dissimilarity in electrocardiogram phase space,” Pattern Recognition, vol. 42, no. 9, pp. 1824–1831, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. S.-C. Fang and H.-L. Chan, “QRS detection-free electrocardiogram biometrics in the reconstructed phase space,” Pattern Recognition Letters, vol. 34, no. 5, pp. 595–602, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. I. Nejadgholi, M. H. Moradi, and F. Abdolali, “Using phase space reconstruction for patient independent heartbeat classification in comparison with some benchmark methods,” Computers in Biology and Medicine, vol. 41, no. 6, pp. 411–419, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. H. Wang, C. Yuan, G. Luo, W. Hu, and C. Sun, “Action recognition using linear dynamic systems,” Pattern Recognition, vol. 46, no. 6, pp. 1710–1718, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. A. López-Méndez and J. R. Casas, “Model-based recognition of human actions by trajectory matching in phase spaces,” Image and Vision Computing, vol. 30, no. 11, pp. 808–816, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. F. Takens, “Detecting strange attractors in turbulence,” in Dynamical Systems and Turbulence, Lecture Notes in Mathematics, pp. 366–381, Springer, Berlin, Germany, 1981. View at Publisher · View at Google Scholar
  30. L. Cao, “Practical method for determining the minimum embedding dimension of a scalar time series,” Physica D: Nonlinear Phenomena, vol. 110, no. 1-2, pp. 43–50, 1997. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Ali, A. Basharat, and M. Shah, “Chaotic invariants for human action recognition,” in Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV '07), pp. 1–8, Rio de Janeiro, Brazil, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  32. L.-M. Xia, J.-X. Huang, and L.-Z. Tan, “Human action recognition based on chaotic invariants,” Journal of Central South University, vol. 20, no. 11, pp. 3171–3179, 2013. View at Publisher · View at Google Scholar · View at Scopus
  33. I. Laptev, Local spatio-temporal image features for motion interpretation [Ph.D. thesis], Computational Vision and Active Perception Laboratory (CVAP), NADA, KTH, Stockholm, Sweden, 2004.
  34. R. Guangbo, Z. Jie, M. Yi, and Z. Rong’er, “Generative model based semi-supervised learning method of remote sensing image classification,” Journal of Remote Sensing, vol. 14, no. 6, pp. 1097–1104, 2010. View at Google Scholar
  35. N. F. Lepora, M. Evans, C. W. Fox, M. E. Diamond, K. Gurney, and T. J. Prescott, “Naive Bayes texture classification applied to whisker data from a moving robot,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '10), pp. 1–8, July 2010.
  36. L. Liu, L. Shao, and P. Rockett, “Human action recognition based on boosted feature selection and naive Bayes nearest-neighbor classification,” Signal Processing, vol. 93, no. 6, pp. 1521–1530, 2013. View at Publisher · View at Google Scholar · View at Scopus
  37. M. D. Rodriguez, J. Ahmed, and M. Shah, “Action MACH: a spatio-temporal maximum average correlation height filter for action recognition,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), Anchorage, Alaska, USA, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  38. F. Martínez-Contreras, C. Orrite-Uruñuela, E. Herrero-Jaraba, H. Ragheb, and S. A. Velastin, “Recognizing human actions using silhouette-based HMM,” in Proceedings of the 6th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS '09), pp. 43–48, Genova, Italy, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. A. A. Chaaraoui, P. Climent-Pérez, and F. Flórez-Revuelta, “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 · View at Scopus
  40. J. Zhang and S. Gong, “Action categorization by structural probabilistic latent semantic analysis,” Computer Vision and Image Understanding, vol. 114, no. 8, pp. 857–864, 2010. View at Publisher · View at Google Scholar · View at Scopus