用3D模型和运动约束，从部分观测中补全小鼠行为追踪。
Tracking Mouse from Incomplete Body-Part Observations and Deep-Learned Deformable-Mouse Model Motion-Track Constraint for Behavior Analysis
- 融合多视角视频，通过3D三角化与束调整估计身体部位位置。
- 引入深度学习的鼠体运动模型与轨迹平滑约束，提升追踪完整性。
- 适合动物行为分析研究者，尤其关注复杂遮挡下的追踪任务。
由于遮挡导致视频中小鼠身体部位的追踪常不完整，从而影响后续行为分析。本文提出一种概念性方法，通过全局外部相机方位整合多视角视频；利用3D三角化与束调整估算身体部位位置。通过引入3D小鼠模型、深度学习的身体部位运动模型以及全局轨迹平滑性约束，实现整体3D轨迹重建的一致性。最终得到的3D身体及身体部位轨迹比原始单帧检测结果更加完整，显著提升了动物行为分析的可靠性。
原文摘要 · Abstract (English)
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.
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