Multi-animal pose estimation and tracking with DeepLabCut

Abstract

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having extremely similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, a popular open source pose estimation toolbox, and provide high-performance animal assembly and tracking—features required for robust multi-animal scenarios. Furthermore, we integrate the ability to predict an animal’s identity directly to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.