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Incremental trajectory aggregation in video sequences

This article introduces new similarity measures between trajectories, in order to detect uncommon behaviors. These measures are used to find the most common trajectories in a sequence, using an implicit aggregation method. They may be applied to trajectories of objects tracked in real time. Moreover, by combining one or more measures, it is possible to variate the impact of the temporal dimension - velocity along a trajectory. Our experiments show that the measures are able to properly identify rare trajectories in a video, as well as to detect the most frequent ones.