  This paper presents a novel approach for automatic recognition of group
activities for video surveillance applications. We propose to use a group
representative to handle the recognition with a varying number of group
members, and use an Asynchronous Hidden Markov Model (AHMM) to model the
relationship between people. Furthermore, we propose a group activity detection
algorithm which can handle both symmetric and asymmetric group activities, and
demonstrate that this approach enables the detection of hierarchical
interactions between people. Experimental results show the effectiveness of our
approach.
