A generic framework for video understanding applied to group behavior recognition
This addresses automated monitoring of group activities in public spaces like airports and subways, but it is incremental as it builds on existing tracking and clustering methods.
The paper tackles group behavior recognition in video surveillance by detecting and tracking individuals, clustering trajectories with Mean-Shift, and using a formal event description language, achieving successful validation on 4 camera views from 3 datasets including an airport, subway, and shopping center.
This paper presents an approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior. This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence. First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm. A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language. The group events recognition approach is successfully validated on 4 camera views from 3 datasets: an airport, a subway, a shopping center corridor and an entrance hall.