Human Action Recognition Based on Tracking Features
Visual recognition of human actions in image sequences is an active field of research. However, most recent published methods use complex models and heuristics of the human body as well as to classify their actions. Our approach follows a different strategy. It is based on simple feature extraction from descriptors obtained from a visual tracking system. The tracking system is able to bring some useful information like position and size of the subject at every time step of a sequence, and in this paper we show that, the evolution of some of these features is enough to classify an action in most of the cases.
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- 2.Schindler, K., Gool, L.: Action Snippets: How many frames does human action recognition require? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008) (2008)Google Scholar
- 4.Zhou, H., Wang, L., Suter, D.: Human action recognition by feature-reduced Gaussian preocess classification. Pattern Recognition Letters (2009)Google Scholar
- 5.Lv, F., Nevatia, R.: Single View human Action Recognition using Key pose Matching and Viterbi Path Searching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, June 17-22, pp. 1–8 (2007)Google Scholar
- 6.Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, August 23-26, vol. 3, pp. 32–36 (2004)Google Scholar
- 7.Parameswaran, V., Chellappa, R.: View invariants for human action recognition. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 18-20, vol. 2, pp. II- 613–II-619 (2003)Google Scholar
- 8.Yan, K., Sukthankar, R., Hebert, M.: Efficient visual event detection using volumetric features. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, October 17-21, vol. 1, pp. 166–173 (2005)Google Scholar
- 9.Ali, S., Basharat, A., Shah, M.: Chaotic Invariants for Human Action Recognition. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, October 14-21, pp. 1–8 (2007)Google Scholar
- 10.Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, August 23-26, vol. 3, pp. 32–36 (2004)Google Scholar
- 13.Moscato, P.: Memetic Algorithms: a short introduction. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 219–234. McGraw Hill, New York (1999)Google Scholar
- 18.Carpenter, J., Clifford, P., Fearnhead, P.: Building robust simulation based filters for evolving data sets. Tech. Rep., Dept. Statist., Univ. Oxford, Oxford, U.K (1999)Google Scholar