Abstract
This paper proposes a recognition method of human actions in video by adding new features, the joint angle acceleration to the feature space. In this method, human body is described as three-dimensional skeletons. The features consist of vectors of several important joint angles on the human body and the joint angle accelerations are also considered as a part of features. Hidden Markov Model (HMM) is used as classification scheme. The HMM models are trained by sequences extracted from the CMU graphics lab motion capture database. This method is invariant to scale, coordinate system and transition. A system is implemented to recognize 4 different types of actions (walk, run, jump and jumping jack) both on the dataset of CMU and Weizmann[7]. Each video clip contains a single action type. The experimental results show excellent performance of the proposed approach. A maximum 10.3% accuracy gain can be achieved by our method compared with the method without considering acceleration.
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
Black, M.J.: Explaining Optical Flow Events with Parameterized Spatio-Temporal Models. In: Computer Vision and Pattern Recognition, vol. 1, pp. 1326–1332 (1999)
Efros, A.A., Berg, A.C., Mori, G., Malik, J.: Recognizing Action at a Distance. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 726–733. IEEE (2003)
Chomat, O., Martin, J., Crowley, J.L.: A Probabilistic Sensor for the Perception and the Recognition of Activities. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 487–503. Springer, Heidelberg (2000)
Zelnik-Manor, L., Irani, M.: Event-Based Analysis of Video. In: Computer Vision and Pattern Recognition, pp. 123–130 (2001)
Chen, H.S., Chen, H.T., Chen, Y.W., et al.: Human action recognition using star skeleton. In: Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks, pp. 171–178. ACM (2006)
Fujiyoshi, H., Lipton, A.J.: Real-Time Human Motion Analysis by Image Skeletonization. In: Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision, pp. 15–21 (1998)
Blank, M., Gorelick, L., Shechtman, E., et al.: Actions as space-time shapes. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1395–1402. IEEE (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, S., Zhang, L. (2013). Hidden Markov Model for Action Recognition Using Joint Angle Acceleration. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-42051-1_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
eBook Packages: Computer ScienceComputer Science (R0)