Optical Mouse: 3D Mouse Pose From Single-View Video
Bo Hu, Bryan Seybold, Shan Yang, David Ross, Avneesh Sud, Graham Ruby,, Yi Liu

TL;DR
This paper introduces a novel method to accurately estimate 3D mouse poses from single-view videos, enabling non-invasive health monitoring and improved behavioral analysis.
Contribution
The proposed approach is the first to infer detailed 3D mouse poses from monocular videos, enhancing health assessment capabilities in animal models.
Findings
Accurately estimates 3D mouse pose from monocular video
Improves classification of health-related attributes over 2D data
Estimates stride length despite occlusions
Abstract
We present a method to infer the 3D pose of mice, including the limbs and feet, from monocular videos. Many human clinical conditions and their corresponding animal models result in abnormal motion, and accurately measuring 3D motion at scale offers insights into health. The 3D poses improve classification of health-related attributes over 2D representations. The inferred poses are accurate enough to estimate stride length even when the feet are mostly occluded. This method could be applied as part of a continuous monitoring system to non-invasively measure animal health.
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Taxonomy
TopicsHuman Pose and Action Recognition · Diabetic Foot Ulcer Assessment and Management · Bat Biology and Ecology Studies
