Abnormal motion detection in video using statistics of spatiotemporal local kinematics pattern

Biomedical studies show that mutant genes in transgenic mutant fishes can lead to muscle disorders so that their swimming capabilities are affected. This paper studies the automatic detection of abnormal mutant fishes by analyzing their movements in the video. To differentiate between normal fish and mutant fish, a new feature extraction method, called spatiotemporal local kinematics pattern (STLKP), is proposed in this paper to provide discriminative spatiotemporal kinematics measurements of fish body movements. Furthermore, the histogram of the proposed STLKP features is incorporated into a motion classification approach to identify whether the fish is normal or a mutant. A large collection of real-world recorded videos is used in experiments to demonstrate that the proposed approach outperforms the conventional approaches to provide more accurate abnormal motion detection performance.