Group Event Detection with a Varying Number of Group Members for Video Surveillance
This addresses automated monitoring of group behaviors in surveillance, but appears incremental with hybrid methods.
The paper tackles group activity recognition in video surveillance by proposing a group representative approach to handle varying group sizes and an Asynchronous Hidden Markov Model to model relationships, achieving effective detection of hierarchical interactions.
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.