Automatic detection of abnormal behavior in CCTV cameras is important to improve the security in crowded
environments, such as shopping malls, airports and railway stations. This behavior can be characterized at different time
scales, e.g., by small-scale subtle and obvious actions or by large-scale walking patterns and interactions between people.
For example, pickpocketing can be recognized by the actual snatch (small scale), when he follows the victim, or when he
interacts with an accomplice before and after the incident (longer time scale). This paper focusses on event recognition
by detecting large-scale track-based patterns. Our event recognition method consists of several steps: pedestrian
detection, object tracking, track-based feature computation and rule-based event classification. In the experiment, we
focused on single track actions (walk, run, loiter, stop, turn) and track interactions (pass, meet, merge, split). The
experiment includes a controlled setup, where 10 actors perform these actions. The method is also applied to all tracks
that are generated in a crowded shopping mall in a selected time frame. The results show that most of the actions can be
detected reliably (on average 90%) at a low false positive rate (1.1%), and that the interactions obtain lower detection
rates (70% at 0.3% FP). This method may become one of the components that assists operators to find threatening
behavior and enrich the selection of videos that are to be observed.