Event detection in underwater domain by exploiting fish trajectory clustering

In this paper we propose a clustering-based approach for the analysis of fish trajectories in real-life unconstrained underwater videos, with the purpose of detecting behavioural events; in such a context, both video quality limitations and the motion properties of the targets make the trajectory analysis task for event detection extremely difficult. Our approach is based on the k-means clustering algorithm and allows to group similar trajectories together, thus providing a simple way to detect the most used paths and the most visited areas, and, by contrast, to identify trajectories which do not fall into any common clusters, therefore representing unusual behaviours. Our results show that the proposed approach is able to separate trajectory patterns and to identify those matching predefined behaviours or which are more likely to be associated to new/anomalous behaviours.