AlphaTracker: a multi-animal tracking and behavioral analysis tool
Zexin Chen, Ruihan Zhang, Hao-Shu Fang, Yu E. Zhang, Aneesh Bal, Haowen Zhou, Rachel R. Rock, Nancy Padilla-Coreano, Laurel R. Keyes, Haoyi Zhu, Yong–Lu Li, Takaki Komiyama, Kay M. Tye, Cewu Lu
Abstract
Computer vision has emerged as a powerful tool to elevate behavioral research. This protocol describes a computer vision machine learning pipeline called AlphaTracker, which has minimal hardware requirements and produces reliable tracking of multiple unmarked animals, as well as behavioral clustering. AlphaTracker pairs a top-down pose-estimation software combined with unsupervised clustering to facilitate behavioral motif discovery that will accelerate behavioral research. All steps of the protocol are provided as open-source software with graphic user interfaces or implementable with command-line prompts. Users with a graphical processing unit (GPU) can model and analyze animal behaviors of interest in less than a day. AlphaTracker greatly facilitates the analysis of the mechanism of individual/social behavior and group dynamics.