In the field of event analysis, the detection of anomalous events has often been based on the creation of a model representing the most common patterns of activity detected within a monitored scene. This way, anomalous events can, be identified by comparison with the model as patterns differing from typical events. In particular trajectories of moving objects have often been. used as a feature for anomalous event detection. In this paper we propose a combination of clustering and SVM techniques in order to automatically detect anomalous trajectories(1).
