Francisco Romero Ferrero and colleagues have developed idtracker.ai, an algorithm and software for tracking individuals in large collectives of unmarked animals, recently described in Nature Methods.
Tracking individual animals in large collective groups can give interesting insights to behavior, but has proven to be a challenge for analysis. With advances in artificial intelligence and tracking software, it has become increasingly easier to collect such information from video data. Ferrero et al. have developed an algorithm and tracking software that features two deep networks. The first tracks animal identification and the second tracks when animals touch or cross paths in front of one another. The software has been validated to track individuals with high accuracy in cohorts of up to 100 animals with diverse species from rodents to zebrafish to ants. This software is free, fully-documented and available online with additional jupyter notebooks for data analysis.
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. Project portal RRID:SCR_021417; Software RRID:SCR_021499
Read the recent Nature Methods article, BioRXiv preprint, and a great video of idtracker.ai tracking 100 zebrafish!
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