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.
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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
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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
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