PrecisionTrack: A Platform for Automated Long-Term Social Behavior Analysis in Naturalized Environments

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, studying socially interacting animals in naturalistic environments remains technically challenging, as current approaches struggle to simultaneously maintain subject identity, extract behavior, and characterize social interactions over prolonged periods. Here, we present PrecisionTrack, an open-source and fully integrated framework designed for real-time multi-animal tracking, behavioral analysis, and social interaction inference in large groups of interacting animals. PrecisionTrack achieves high spatiotemporal accuracy in crowded and highly occlusive environments while maintaining robust long-term identity tracking and low-latency processing. To extend behavioral inference beyond pose estimation, we developed the Multi-animal Action Recognition Transformer (MART), a transformer-based architecture enabling real-time subject-level action recognition, and Graph-MART (G-MART), a graph neural network module that infers directed social interactions and interaction partners within groups. In addition, PrecisionTrack supports quantitative analysis of evolving social networks across time, enabling investigation of the temporal organization and stability of social dynamics in naturalistic settings. The entire framework is open source and accompanied by standardized workflows and documentation, enabling users to train, evaluate, and deploy custom behavioral analysis pipelines across species and experimental contexts. PrecisionTrack provides a scalable platform for quantitative investigation of complex social behaviors at a resolution and duration not accessible with existing methods.