Multi-view image-based behavior classification of wet-dog shake in Kainate rat model - Negrete, Salvador Blanco; Arai, Hirofumi; Natsume, Kiyohisa; Shibata, Tomohiro (Frontiers in Behavioral Neuroscience, 2023)
In this work, we present a multi-view animal behavior detection system based on image classification and use it to detect rats' wet-dog shake behavior (WDS). No animal behavior detection system has included WDS despite being relevant in many animal disease models like acute seizures, morphine abstinence, and nicotine withdrawal, among others. Compared to previous approaches that use a single view and rely on artificial features (feature engineering), our system uses a novel time-multi-view fusion scheme that does not use feature engineering. This approach makes it flexible to adapt to other animals and behaviors. It can use one or more views for higher accuracy. We tested our framework to classify WDS behavior in rats. We compare the results using different amounts of cameras and show that the use of additional views increases the performance of WDS behavioral classification. (0.91 precision and 0.86 recall with three cameras).