Abstract
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, how to set them up in a lab that is new to video recording methods, and some issues that should be addressed by developers and advanced users, including the need to openly share datasets and code, how to compare algorithms and their parameters, and the need for documentation and community-wide standards. We hope to encourage more widespread use and continued development of the tools. They have tremendous potential for accelerating scientific progress for understanding the brain and behavior.
Abstract (translated)
URL
https://arxiv.org/abs/2204.02842
