2025/08/15 by Isaac Robinson, George Glidden-Handgis, Neekesh Panchal +2 · 1 voice
Biochemistry, Genetics and Molecular Biology · Environmental Science · Veterinary · #Animal Behavior and Welfare Studies #Human-Animal Interaction Studies #Wildlife Ecology and Conservation
paper · pdf · doi:10.1101/2025.08.11.669786
openalex publication_date 2025/08/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/14
Abstract Recent advances in computer vision have enabled the development of automated animal behavior observation tools. Several software packages currently exist for concurrently tracking pose in multiple animals; however, existing tools still face challenges in maintaining animal identities across frames and can demand extensive human oversight and editing. Here we report on DIPLOMAT, a D eep learning-based, I dentity- P reserving, L abeled- O bject M ulti- A nimal T racker, which implements automated algorithms to improve identity continuity, supplemented by an efficient human interface to help eliminate remaining errors. DIPLOMAT is designed to perform multi-animal tracking by building on the per-frame pose prediction models of two state-of-the-art tools, DeepLabCut and SLEAP, applying novel methods to tolerate occlusion and preserve animal identity across frames. Notable features include leveraging model-derived positional probabilities to compute independent maximum probability traces across frames of a video, use of video-specific skeletal constraints, and implementation of an efficient user interface for resolving errors. On the MABe mouse tracking benchmark, automated tracking with DIPLOMAT reduces body identity swaps by >75%, while remaining errors are easily eradicated with manual correction.