PrecisionTrack: Reliable Tracking of Large Groups of Animals Interacting in Complex Environments Over Extended Periods
Coulombe, V.; Roussel, D.-A.; Monfared, M. S.; Leboulleux, Q.; Peralta, M. R.; Aghel, K.; Gosselin, B.; Labonte, B.
Show abstract
Large-scale ethological behavioral studies can provide insights into the neuronal processes underlying complex and social behaviors, potentially opening new avenues for mental health research. However, these studies remain tedious and time-consuming, relying on manual annotation of predefined features. Despite recent advances, automatically monitoring complex behaviors in socially interacting animals remains challenging, limiting the scope and scale of experiments. Here, we present PrecisionTrack, an open-source framework designed to track and monitor large groups of interacting animals over prolonged periods. PrecisionTrack shows high spatiotemporal accuracy in crowded social settings, maintains consistent tracking performance over time and supports real-time, online responsiveness. We also introduce the Multi-animal Action Recognition Transformer (MART), which can be seamlessly integrated with PrecisionTrack to extract behavioral insights beyond spatiotemporal localization, including individual actions and social interactions. The entire system is open source and accessible. Overall, combined with a step-by-step guide, PrecisionTrack enables users to train, test and deploy their own social behavior monitoring systems, offering novel possibilities to study complex social behaviors in crowded naturalized environments.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- OpenMonkeyStudio: Automated Markerless Pose Estimation in Freely Moving Macaques 95%
- Large-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models 95%
- Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.