  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.
