Tracking Mouse from Incomplete Body-Part Observations and Deep-Learned Deformable-Mouse Model Motion-Track Constraint for Behavior Analysis
This addresses incomplete tracking for animal behavior researchers, but it appears incremental as it builds on existing multi-view and model-based approaches.
The paper tackles incomplete mouse body part tracking in video due to occlusions by integrating multi-view videos with 3D triangulation and a deep-learned deformable mouse model with motion constraints, resulting in substantially more complete 3D track estimates that improve behavior analysis.
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.