Trajectory clustering and its applications for video surveillance

Abstract

In this paper we present a trajectory clustering method suited for video surveillance and monitoring systems. The clusters are dynamic and built in real-time as the trajectory data is acquired, without the need of an off-line processing step. We show how the obtained clusters can be successfully used both to give proper feedback to the low-level tracking system and to collect valuable information for the high-level event analysis modules.