Hierarchal Decomposition for Unusual Fish Trajectory Detection

Cigdem Beyan, Robert Fisher

Research output: Chapter in Book/Report/Conference proceedingChapter


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.
Original languageEnglish
Title of host publicationComputer Vision and Pattern Recognition in Environmental Informatics
EditorsJun Zhou, Xiao Bai, Terry Caelli
PublisherIGI Global
Number of pages21
ISBN (Electronic)9781466694361
ISBN (Print)9781466694354
Publication statusPublished - 2016


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