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.

翻译：近期发展的视频分析方法，特别是姿态估计与行为分类模型，正在推动神经科学、行为学等领域的行为量化向更精确、可扩展、可复现的方向转变。这些工具突破了长期以来人工逐帧评分和传统"质心追踪"算法的局限性，实现了大规模视频分析。开源视频采集与分析工具的扩展催生了理解行为的新实验范式。本文系统梳理了当前可用的开源视频分析工具，并为刚接触视频录制的新实验室提供方法搭建指南。我们同时探讨了开发和使用视频分析方法的最佳实践，包括社区通用标准、数据集与代码开放共享的关键需求、视频分析方法更广泛的技术对比，以及面向新用户的方法文档优化建议。我们鼓励学界更广泛地采用并持续开发这些工具，它们具有加速脑科学与行为科学研究的巨大潜力。