Anomaly detection of fish school behavior based on features statistical and optical flow methods

Abstract: The behavior of fishes is very sensitive to the changes of the parameters of the environment, such as temperature, dissolved oxygen, light, and so on. The anomaly detection of fish school behavior can not only discover the relationship between the fish behaviors and the environmental parameters, but also provide an important method and tool for fish health monitoring and early warning. Moreover, it is very meaningful for the study of the mechanism of fish behavior and promotion of the informatization level in aquaculture. By using computer vision technology and based on a statistical method of motion features, the anomaly detection of fish school behavior was studied. The zebra fish was selected as the study object in this paper. First, based on the foreground object detection method with a threshold value method, the backgrounds were removed from the original video images to reduce the influence of noises. Secondly, by the Lucas-Kanade optical flow method, which is based on the local deference method and has better performance, the vectors of motion behavior could be obtained in different temporal and spatial conditions. Thirdly, from these data, the joint histograms and joint probability distributions of turning angles and velocities were calculated. Since from the practical point of view, the anomalous behaviors of a fish school mainly include the change of the moving velocity and the chaos of the moving direction. This is the reason to select turning angle and velocity as the features to analyze. At last, the NMI method and the LDOF methods were applied to study the anomaly detection of fish school behavior. By choosing proper threshold values, the NMI method and the LDOF methods can implement the behavior detection of the zebra fish school. The experiments showed that the accuracy rates of the NMI method and the LDOF method for anomaly detection of fish school behavior can achieve 99.92% and 99.88%, respectively, which implies that both of the two methods have better effects.