Tracking mouse body parts in video is often incomplete due to occlusions such that - e.g. - subsequent action and behavior analysis is impeded. In this conceptual work, videos from several perspectives are integrated via global exterior camera orientation; body part positions are estimated by 3D triangulation and bundle adjustment. Consistency of overall 3D track reconstruction is achieved by introduction of a 3D mouse model, deep-learned body part movements, and global motion-track smoothness constraint. The resulting 3D body and body part track estimates are substantially more complete than the original single-frame-based body part detection, therefore, allowing improved animal behavior analysis.
@article{arxiv.2501.11030,
title = {Tracking Mouse from Incomplete Body-Part Observations and Deep-Learned Deformable-Mouse Model Motion-Track Constraint for Behavior Analysis},
author = {Olaf Hellwich and Niek Andresen and Katharina Hohlbaum and Marcus N. Boon and Monika Kwiatkowski and Simon Matern and Patrik Reiske and Henning Sprekeler and Christa ThöneReineke and Lars Lewejohann and Huma Ghani Zada and Michael Brück and Soledad Traverso},
journal= {arXiv preprint arXiv:2501.11030},
year = {2025}
}