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:最近开发的视频分析方法,特别是图像估计和行为分类模型,正在改变行为量化方法,使之更加精确、可伸缩,并在神经科学和神学等领域可以复制。这些工具克服了人工评分视频框架和传统的“质量中心”跟踪算法的长期局限性,以便能够进行规模的视频分析。视频获取和分析的开放源工具的扩大导致了理解行为的新的实验方法。在这里,我们审查了现有的视频分析开放源工具,并讨论了如何为新到视频记录的实验室建立这些方法。我们还讨论了开发和使用视频分析方法的最佳做法,包括全社区标准和开放分享数据集和代码的关键需求,对视频分析方法进行更广泛的比较,以及改进这些方法的文献记录,特别是对新用户而言。我们鼓励更广泛地采用和继续开发这些工具,这些工具对于加快了解大脑和行为的科学进步具有巨大的潜力。