Hierarchal decomposition for unusual fish trajectory detection

Fish behavior analysis is presented using an unusual trajectory detection method. The proposed method is based on a hierarchy which is formed using the similarity of clustered and labeled data applying hierarchal data decomposition. The fish trajectories from unconstrained underwater videos are classified as normal and unusual where normal trajectories represents common behaviors of fish and unusual trajectories represent rare behaviors. A new trajectory is classified using the constructed hierarchy where different heuristics are applicable. The main contribution of the proposed method is presenting a novel supervised approach to unusual behavior detection (where many methods in this field are unsupervised) which demonstrates significantly improved results.