标题： 从不完整的身体部位观测中跟踪小鼠以及用于行为分析的深度学习可变形小鼠模型运动轨迹约束 显示英文标题

标题： Tracking Mouse from Incomplete Body-Part Observations and Deep-Learned Deformable-Mouse Model Motion-Track Constraint for Behavior Analysis

摘要： 在视频中跟踪小鼠身体部位通常由于遮挡而不完整，例如，后续的动作和行为分析受到阻碍。在本概念性工作中，通过全局外部相机方向整合来自多个视角的视频；身体部位位置通过三维三角测量和光束法平差进行估计。通过引入三维小鼠模型、深度学习的身体部位运动以及全局运动轨迹平滑性约束，实现了整体三维轨迹重建的一致性。所得的三维身体和身体部位轨迹估计比原始基于单帧的身体部位检测要完整得多，因此，允许改进动物行为分析。 显示英文摘要

摘要： 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.