There are a number of video-based behavior tracking packages available these days, including several that include methods for pose estimation. Such methods are incredibly powerful for many questions in behavioral neuroscience; however, sometimes a clean, simplified, and versatile animal tracker is the perfect tool to address sophisticated questions. Violette Chiara and Sin-Yeon Kim and colleagues developed and shared a Python-based video tracking program, called AnimalTA, that allows for tracking and analyzing animal movement in complex environments. This program may be useful for researchers who are interested simply in movement paths within an operant chamber or homecage, where more complicated algorithms may not be necessary to extract that sort of data. The code is shared on Github and also contains useful information and tutorials for this project. It has a Windows installer and also runs on Mac and Linux PCs. It does tracking using an adaptive thresholding algorithm or by using simpler background subtraction. The developers show that the program works in natural settings with a complex background and could be useful for many neuroscience applications. Read more in the publication, check out the GitHub repository, and explore the website!
This research tool was created by your colleagues. Please acknowledge the Principal Investigator, cite the article in which the tool was described, and include an RRID in the Materials and Methods of your future publications. RRID:SCR_023784
Access the software from GitHub!
Check out the repository on GitHub.
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