Detection of Abnormal Fish Trajectories Using a Clustering Based Hierarchical Classifier

We address the analysis of fish trajectories in unconstrained underwater videos to help marine biologist to detect new/rare fish behaviours and to detect environmental changes which can be observed from the abnormal behaviour of fish. The fish trajectories are separated into normal and abnormal classes which indicate the common behaviour of fish and the behaviours that are rare/ unusual respectively. The proposed solution is based on a novel type of hierarchical classifier which builds the tree using clustered and labelled data based on similarity of data while using different feature sets at different levels of hierarchy. The paper presents a new method for fish trajectory analysis which has better performance compared to state-of-the-art techniques while the results are significant considering the challenges of underwater environments, low video quality, erratic movement of fish and highly imbalanced trajectory data that we used. Moreover, the proposed method is also powerful enough to classify highly imbalanced real-world datasets.