Deep learning approaches for head pose estimation in sports impacts

Abstract Videogrammetry can quantify head acceleration events in sport, but because standard datasets lack the large rotations, rapid motion, and frequent occlusion characteristic of sports collisions, the accuracy of modern deep learning pose estimators in this context remains unclear. This study addresses this gap by benchmarking three models for monocular head pose estimation during controlled football headers: a direct head pose regressor, an end-to-end face reconstruction model, and a full-