Assessing Water Quality by Video Monitoring Fish Swimming Behavior

Animals are known to alter their behavior in response to changes in their environments. Therefore, automatic visual monitoring of animal behavior is currently of great interest because of its many applications. In this paper, a video-based system is proposed for analyzing the swimming patterns of fishes so that the presence of toxic in the water can be inferred. This problem is challenging, among other reasons, because how fishes react when swimming in contaminated water is neither really known nor well defined. A novel use of recurrence plots is proposed, and very compact and simple descriptors based on these recurrence representation are found to be highly discriminative between videos of fishes in clean and polluted water.