Open-source tools for behavioral video analysis: Setup, methods, and best practices.
Open-source tools for behavioral video analysis: Setup, methods, and best practices.
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DOI：
10.7554/elife.79305
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发表时间：
2023-03-23
期刊：
影响因子：
7.7
通讯作者：
Laubach M
中科院分区：
文献类型：
--
作者：
Luxem K;Sun JJ;Bradley SP;Krishnan K;Yttri E;Zimmermann J;Pereira TD;Laubach M
最近开发的视频分析方法，特别是姿势估计和行为分类模型，正在改变行为量化，使其在神经科学和行为学等领域更加精确，可扩展和可重现。这些工具克服了视频帧手动评分和传统“质心”跟踪算法的长期局限性，从而实现大规模视频分析。用于视频采集和分析的开源工具的扩展导致了新的实验方法来理解行为。在这里，我们回顾了目前可用的视频分析开源工具，并讨论了如何为视频记录的新实验室设置这些方法。我们还讨论了开发和使用视频分析方法的最佳实践，包括开放共享数据集和代码的社区标准和关键需求，视频分析方法的更广泛比较，以及这些方法的更好文档，特别是对于新用户。我们鼓励更广泛地采用和继续开发这些工具，这些工具在加速理解大脑和行为的科学进步方面具有巨大的潜力。
Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional ‘center of mass’ tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.