  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.
