Abstract
Activity recognition is one of the most challenging problems in the high-level computer vision field. In this paper, we present a novel approach to interacting activity recognition based on dynamic Bayesian network (DBN). In this approach the features representing the human activities are divided into two classes: global features and local features, which are on two different spatial scales. To model and recognize human interacting activities, we propose a hierarchical durational-state DBN model (HDS-DBN). HDS-DBN combines the global features with local ones organically and reveals structure of interacting activities well. The effectiveness of this approach is demonstrated by experiments.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aggarwal, J.K., Park, S.: Human Motion: Modeling and Recognition of Actions and Interactions. In: Proc. 3DPVT, pp. 640–647 (2004)
Wang, L., Hu, W., Tan, T.: Recent developments in human motion analysis. Pattern Recognition 36, 585–601 (2003)
Oliver, N.M., Rosario, B., Pentland, A.P.: A Bayesian computer vision system for modeling human interactions. IEEE Trans. PAMI 22, 831–843 (2000)
Brand, M., Oliver, N., Pentland, A.: Coupled hidden Markov models for complex action recognition. In: Proc. CVPR, pp. 994–999 (1997)
Park, S., Aggarwal, J.K.: A hierarchical Bayesian network for event recognition of human actions and interactions. Multimedia Systems 10, 164–179 (2004)
Gong, S., Xiang, T.: Recognition of group activities using dynamic probabilistic networks. In: Proc. ICCV, pp. 742–749 (2003)
Liu, X., Chua, C.: Multi-agent activity recognition using observation decomposed hidden markov models. Image and vision computing 24, 166–175 (2006)
Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6, 461–464 (1978)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Du, Y., Chen, F., Xu, W., Zhang, W. (2006). Interacting Activity Recognition Using Hierarchical Durational-State Dynamic Bayesian Network. In: Zhuang, Y., Yang, SQ., Rui, Y., He, Q. (eds) Advances in Multimedia Information Processing - PCM 2006. PCM 2006. Lecture Notes in Computer Science, vol 4261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922162_22
Download citation
DOI: https://doi.org/10.1007/11922162_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48766-1
Online ISBN: 978-3-540-48769-2
eBook Packages: Computer ScienceComputer Science (R0)