- Chen, Zexin;
- Zhang, Ruihan;
- Fang, Hao-Shu;
- Zhang, Yu;
- Bal, Aneesh;
- Zhou, Haowen;
- Rock, Rachel;
- Padilla-Coreano, Nancy;
- Keyes, Laurel;
- Zhu, Haoyi;
- Li, Yong-Lu;
- Komiyama, Takaki;
- Tye, Kay;
- Lu, Cewu
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.