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. 2023 May 30;17:1111908. doi: 10.3389/fnbeh.2023.1111908

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Copyright © 2023 Chen, Zhang, Fang, Zhang, Bal, Zhou, Rock, Padilla-Coreano, Keyes, Zhu, Li, Komiyama, Tye and Lu.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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AlphaTracker surpasses the performance of both DeepLabCut and SLEAP in tracking two mice in a home cage. (A–C) Show the Average Precision (AP), Multi-Object Tracking Precision (MOTP), and Multi-Object Tracking Accuracy (MOTA) metrics for different keypoints of AlphaTracker results. The metrics were evaluated for different amounts of training frames (25, 50, 100, 200, 300, and 600 frames) using a 200-frame evaluation dataset. Different colors represent different keypoints including snouts (blue), ears (yellow) and tailbases (green) and the total metrics (red). (D–F) Show the evaluation results for AlphaTracker (red), SLEAP (green), and DeepLabCut (blue), with connected dots representing the total metric and the unconnected dots representing the metric for individual body parts.