Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wang, L., Suter, D.: Visual learning and recognition of sequential data manifolds with applications to human movement analysis. Computer Vision and Image Understanding 110, 153–172 (2008)
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)
Ahmad, M., Lee, S.W.: Human action recognition using shape and CLG-motion flow from multi-view image sequences. Pattern Recognition 41, 2237–2252 (2008)
Zhou, H., Wang, L., Suter, D.: Human action recognition by feature-reduced Gaussian preocess classification. Pattern Recognition Letters (2009)
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)
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)
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)
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)
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)
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)
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as Space-Time Shapes. Transactions on Pattern Analysis and Machine Intelligence 29(12), 2247–2253 (2007)
Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F Radar & Signal Processing 140 2, 107–113 (1993)
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)
Moscato, P., Cotta, C.: A gentle introduction to Memetic Algorithms. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 105–144. Kluwer Academic Publishers, Boston (2003)
Arulampalam, S.M., Maskell, S., Gordon, N., Clapp, T.: A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Trans. on Signal Processing, 50(2), 174–178 (2002)
Pantrigo, J.J., Hernández, J., Sánchez, A.: Multiple and variable target visual tracking for video-surveillance applications. Pattern Recogn. Lett. 31(12), 1577–1590 (2010)
Hart, W.E., Krasnogor, N., Smith, J.E.: Recent Advances in Memetic Algorithms. Springer, Heidelberg (2005)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hernández, J., Montemayor, A.S., José Pantrigo, J., Sánchez, Á. (2011). Human Action Recognition Based on Tracking Features. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) Foundations on Natural and Artificial Computation. IWINAC 2011. Lecture Notes in Computer Science, vol 6686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21344-1_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-21344-1_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21343-4
Online ISBN: 978-3-642-21344-1
eBook Packages: Computer ScienceComputer Science (R0)