Autonomous Rotating Cameras Boost 3D Wildlife MoCap Yield without Human Operators

Abstract

We present a low-cost, autonomous, rotating-camera system that increases the usable data yield for 3D markerless motion capture of animals in uncontrolled outdoor settings. A lightweight detector (YOLOv4-Tiny) locates the subject at 10 Hz; an Extended Kalman Filter bridges sparse detections to a 50 Hz full-state feedback (FSF) controller, keeping the subject centered without a human operator. The 3D reconstruction backend uses existing markerless 2D keypoints and Full Trajectory Estimation (FTE) with a simple rotation compensation for moving cameras. On field videos of a running human and free-running cheetahs, the rotating cameras captured substantially more usable frames than fixed cameras: +52% for the human sequence (6593 vs. 4333 frames) and +135% across cheetah sequences (2419 vs. 1031 frames). Centering also shifts subject pixel distribution toward the image center, which theoretically lowers 2D keypoint error and thus 3D reprojection error for any pose-estimation backend. We detail the EKF design for sparse/noisy detections, the FSF controller with an integral state, and practical deployment considerations. Results show autonomous centering is a simple, deployable lever to scale outdoor animal mocap without changing downstream reconstruction methods.