Multi-view image-based behavior classification of wet-dog shake in Kainate rat model

Abstract

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The wet-dog shake behavior (WDS) is a short-duration behavior relevant to the study of various animal disease models, including acute seizures, morphine abstinence, and nicotine withdrawal. However, no animal behavior detection system has included WDS. In this work, we present a multi-view animal behavior detection system based on image classification and use it to detect rats’ WDS behavior. Our system uses a novel time-multi-view fusion scheme that does not rely on artificial features (feature engineering) and is 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 and compared the results using different amounts of cameras. Our results show that the use of additional views increases the performance of WDS behavioral classification. With three cameras, we achieved a precision of 0.91 and a recall of 0.86. Our multi-view animal behavior detection system represents the first system capable of detecting WDS and has potential applications in various animal disease models.

Keywords

• wet-dog shake
• rat
• deep learning
• animal behavior
• multi-view
• behavior classification