A filtering mechanism for normal fish trajectories

Understanding fish behavior by extracting normal motion patterns and then identifying abnormal behaviors is important for understanding the effects of environmental change. In the literature, there are many studies on normal/abnormal behavior detection in the areas of human behaviour analysis, traffic surveillance, and nursing home surveillance, etc. However, the literature is very limited in terms of normal/abnormal fish behavior understanding especially when natural habitat applications are considered. In this study, we present a rule based trajectory filtering mechanism to extract normal fish trajectories which potentially helps to increase the accuracy of the abnormal fish behavior detection systems and can be used as a preliminary method especially when the number of abnormal fish behaviors are very small (e.g. 40-50 times smaller) compared to the number of normal fish behaviors and/or when the number of trajectories are huge.