AlphaTracker: A Multi-Animal Tracking and Behavioral Analysis Tool

Cewu Lu, Haowen Zhou, Nancy Padilla-Coreano, Kay M. Tye, Aneesh Bal, Laurel R. Keyes, Yu E. Zhang, Haoshu Fang, Zexin Chen, Ruihan Zhang, Rachel R. Rock
2020-12-06

SCID:  54.1/z3j5t43t
Abstract The advancement of behavioral analysis in neuroscience has been aided by the development of computational tools 1,2 . Specifically, computer vision algorithms have emerged as a powerful tool to elevate behavioral research 3,4 . Yet fully automatic analysis of social behavior remains challenging in two ways. First, existing tools to track and analyze behavior often focus on single animals, not multiple, interacting animals. Second, many available tools are not developed for novice users and require programming experience to run. Here, we unveil a computer vision pipeline called AlphaTracker, which requires minimal hardware requirements and produces reliable tracking of multiple unmarked animals. An easy-to-use user interface further enables manual inspection and curation of results. We demonstrate the practical, real-time advantages of AlphaTracker through the study of multiple, socially-interacting mice.
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2020-12-06
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Cewu Lu
Haowen Zhou
Nancy Padilla-Coreano
Kay M. Tye
Aneesh Bal
Laurel R. Keyes
Yu E. Zhang
Haoshu Fang
Zexin Chen
Ruihan Zhang
Rachel R. Rock
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