Automated Analysis of Wild Fish Behavior in a Natural Habitat

This paper proposes a novel approach for the analysis of movement and behavior of the Plainfin midshipman (Porichthys notatus) in the wild. It is based on underwater video recordings of the fish in their natural habitat taken inside their nests during reproductive months. During this time, alpha male Plainfin midshipmen rarely leave their nests as they are guarding their eggs, so the proposed approach addresses the issue of detecting subtle motion and nesting behavior as the fish remains relatively sedentary. To the best of our knowledge, this is the first paper to propose an automated method to analyze subtle movements of a highly territorial animal in its natural habitat. Motion detection uses the displacement of SURF (Interest point algorithm) key-point movements from frame to frame to analyze the amount of movement by the fish. K-means clustering and other outlier removal techniques are then used to differentiate fish motion from small moving objects in the background and foreground. The analysis of fish behavior uses similarity-based periodicity detection combined with the K-neighbors classifier. Experimental validation with respect to expert-annotated ground truth shows excellent performance for both motion and behavior detection approaches.