Detecting abnormal fish trajectories using clustered and labeled data

We propose an approach for the analysis of fish trajectories in unconstrained underwater videos. Trajectories are classified into two classes: normal trajectories which contain the usual behavior of fish and abnormal trajectories which indicate the behaviors that are not as common as the normal class. The paper presents two innovations: 1) a novel approach to abnormal trajectory detection and 2) improved performance on video based abnormal trajectory analysis of fish in unconstrained conditions. First we extract a set of features from trajectories and apply PCA. We then perform clustering on a subset of features. Based on the clustering, outlier detection is applied to each cluster. Improved results are obtained which is significant considering the challenges of underwater environments, low video quality, and erratic movement of fish.