Table of Contents
ISRN Machine Vision
Volume 2012 (2012), Article ID 872131, 9 pages
http://dx.doi.org/10.5402/2012/872131
Research Article

Chord-Length Shape Features for Human Activity Recognition

Institute for Electronics, Signal Processing and Communications (IESK), Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany

Received 2 August 2012; Accepted 20 September 2012

Academic Editors: M. La Cascia, A. Prati, J. M. Tavares, and C. S. Won

Copyright © 2012 Samy Sadek 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. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “An efficient method for real-time activity recognition,” in Proceedings of the International Conference of Soft Computing and Pattern Recognition (SoCPaR '10), pp. 69–74, Paris, France, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, “Actions as space-time shapes,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), vol. 2, pp. 1395–1402, October 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. R. Cutler and L. S. Davis, “Robust real-time periodic motion detection, analysis, and applications,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 781–796, 2000. View at Publisher · View at Google Scholar · View at Scopus
  4. A. A. Efros, A. C. Berg, G. Mori, and J. Malik, “Recognizing action at a distance,” in Proceedings of the 9th IEEE International Conference on Computer Vision, vol. 2, pp. 726–733, October 2003. View at Scopus
  5. L. Little and J. E. Boyd, “Recognizing people by their gait: The shape of motion,” Journal of Computer Vision, vol. 1, no. 2, pp. 1–32, 1998. View at Google Scholar
  6. W. L. Lu, K. Okuma, and J. J. Little, “Tracking and recognizing actions of multiple hockey players using the boosted particle filter,” Image and Vision Computing, vol. 27, no. 1-2, pp. 189–205, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Sadek, A. AI-Hamadi, M. Elmezain, B. Michaelis, and U. Sayed, “Human activity recognition via temporal moment invariants,” in Proceedings of the 10th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT '10), pp. 79–84, Luxor, Egypt, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “Human activity recognition: a scheme using multiple cues,” in Proceedings of the International Symposium on Visual Computing (ISVC '10), vol. 1, pp. 574–583, Las Vegas, Nev, USA, November 2010.
  9. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “A statistical framework for real-time traffic accident recognition,” Journal of Signal and Information Processing, vol. 1, pp. 70–81, 2010. View at Google Scholar
  10. C. Thurau and V. Hlavac, “Pose primitive based human action recognition in videos or still images,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), 2008.
  11. 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
  12. I. Laptev and P. Pérez, “Retrieving actions in movies,” in Proceedings of the 11th International Conference on Computer Vision (ICCV '07), 2007.
  13. J. Liu and M. Shah, “Learning human actions via information maximization,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), June 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “Towards robust human action retrieval in video,” in Proceedings of the British Machine Vision Conference (BMVC '10), Aberystwyth, UK, September 2010.
  15. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “An action recognition scheme using fuzzy log-polar histogram and temporal self-similarity,” Eurasip Journal on Advances in Signal Processing, vol. 2011, Article ID 540375, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. A. F. Bobick and J. W. Davis, “The recognition of human movement using temporal templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 3, pp. 257–267, 2001. View at Publisher · View at Google Scholar · View at Scopus
  17. E. Shechtman and M. Irani, “Space-time behavior based correlation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), vol. 1, pp. 405–412, June 2005. View at Scopus
  18. 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), June 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Jhuang, T. Serre, L. Wolf, and T. Poggio, “A biologically inspired system for action recognition,” in Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV '07), October 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. 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), June 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. X. Feng and P. Perona, “Human action recognition by sequence of movelet codewords,” in Proceedings of the 1st International Symposium on 3D Data Processing Visualization and Transmission (3DPVT '02), pp. 717–721, 2002.
  22. N. Ikizler and D. Forsyth, “Searching video for complex activities with finite state models,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), June 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. B. Laxton, J. Lim, and D. Kriegmant, “Leveraging temporal, contextual and ordering constraints for recognizing complex activities in video,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), pp. 1–8, 2007.
  24. N. Oliver, A. Garg, and E. Horvitz, “Layered representations for learning and inferring office activity from multiple sensory channels,” Computer Vision and Image Understanding, vol. 96, no. 2, pp. 163–180, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” Journal of Machine Learning Research, vol. 3, no. 4-5, pp. 993–1022, 2003. View at Google Scholar · View at Scopus
  26. D. M. Blei and J. D. Lafferty, “Correlated topic models,” in Advances in Neural Information Processing Systems, vol. 18, pp. 147–154, 2006. View at Google Scholar
  27. T. Hofmann, “Probabilistic latent semantic indexing,” in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '99), pp. 50–57, 1999.
  28. Y. Wang and G. Mori, “Max-Margin hidden conditional random fields for human action recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '09), pp. 872–879, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  29. K. Rapantzikos, Y. Avrithis, and S. Kollias, “Dense saliency-based spatiotemporal feature points for action recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '09), pp. 1454–1461, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. Y. Ke, R. Sukthankar, and M. Hebert, “Efficient visual event detection using volumetric features,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), pp. 166–173, October 2005. View at Publisher · View at Google Scholar · View at Scopus
  31. D. Zhang and G. Lu, “A comparative study of fourier descriptors for shape representation and retrieval,” in Proceedings of the 5th Asian Confenence on Computer Vision (ACCV '02), 2002.
  32. C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), vol. 2, pp. 246–252, June 1999. View at Scopus
  33. 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), June 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. M. Bregonzio, S. Gong, and T. Xiang, “Recognising action as clouds of space-time interest points,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '09), pp. 1948–1955, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. Z. Zhang, Y. Hu, S. Chan, and L. T. Chia, “Motion context: a new representation for human action recognition,” in Proceedings of the 10th European Conference on Computer Vision (ECCV '08), vol. 4, pp. 817–829, 2008. View at Publisher · View at Google Scholar · View at Scopus
  36. J. C. Niebles, H. Wang, and L. Fei-Fei, “Unsupervised learning of human action categories using spatial-temporal words,” International Journal of Computer Vision, vol. 79, no. 3, pp. 299–318, 2008. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Klaser, M. Marszaek, and C. Schmid, “A spatio-temporal descriptor based on 3d-gradients,” in Proceedings of the British Machine Vision Conference (BMVC '08), 2008.
  38. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
  39. C. Schüldt, I. Laptev, and B. Caputo, “Recognizing human actions: a local SVM approach,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04), vol. 3, pp. 32–36, August 2004. View at Scopus