Animal Behavior Classification Using DeepLabCut
Shiori Fujimori, Takaaki Ishikawa, Hiroshi Watanabe · 2020
In this paper, we introduce a method to classify animal behaviors from videos taken by a fixed-point camera. In order to classify animal behavior, it is necessary to detect and track the animals. Conventional approaches for detecting moving objects are based on background subtraction and frame subtraction. Conventional methods are not suitable for detection of animals kept indoors since they are susceptible to sunlight and shadow. We propose a method to track animals and classify their behavior using skeletal information obtained by DeepLabCut. The experimental results show that the proposed method is superior to the conventional method.