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Multi-agent activity recognition using observation decomposed hidden Markov models
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DOI：10.1016/j.imavis.2005.09.024.png)
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To automatically recognize multi-agent activities is a highly challenging task due to the complexity of the interactions between agents. The difficulties in this task stern from two aspects: firstly, the feature vectors derived from input data are of large dimensionality and variable length. Secondly, an efficient mapping of agents from input data to pre-defined activity models, known as agent assignment, is required. This paper presents a new method to model and classify multi-agent activities based on the proposed observation decomposed hidden Markov models (ODHMMs). To handle the feature vectors, we decomposed each original feature vector into a Set Of sub-feature vectors to keep the explored feature space consistent. Agent assignment is realized using a newly introduced parameter, which represents the 'role' of each agent. The experimental results show that the proposed method can successfully classify three-person activities with high accuracy and is less sensitive to incomplete data input. (c) 2005 Elsevier B.V. All rights reserved.
Keyword:
hidden Markov models
activity recognition
visual surveillance
multi-agent activities
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