A broad range of research areas use behavioral analyses. Despite behavior being extensively used, it is quite uncommon for researchers to analyze long-term continuous behavior, especially in social groups, as it is challenging to accurately track individuals and their behaviors. This is even more pressing in social animals such as marmosets, which show many social close contact behaviors while moving in three dimensions. A study in Communications Biology shows a new video-based system combined with Light Detection and Ranging (Lidar) and deep learning to recognize individual marmosets, tracking multiple individuals in a social context. The software was developed to use facial recognition for accurate identification of the individual animals. Then, using Lidar and video recording, the coordinates of each individual were calculated for trajectory tracking. At the same time, an algorithm tracks and classifies individual animal behaviors, especially social behaviors such as grooming. This proof-of-concept study shows the construction and validation of a functional automatic 3D tracking system adapted to marmosets under free-moving conditions. A tracking system that can be used for longer term behavioral analysis to detect behavioral pattern shifts that are characteristic of diseases.
Original reference: Yurimoto, T. et al. Commun. Biol. 7, 216 (2024)
This is a preview of subscription content, access via your institution