Evaluating the accuracy of a Vision-Based Algorithm for Groundline Estimation in Trotting Horses Using Multiple Camera Angles

This dataset supports the study titled “Evaluating the Accuracy of a Vision-Based Algorithm for Groundline Estimation in Trotting Horses Using Multiple Camera Angles”. The data were collected as part of an experimental comparative study evaluating a deep learning-based computer vision algorithm for equine gait analysis. Recordings were made of eight Standardbred trotter mares trotting on a high-speed treadmill using seven iPhones placed at various fixed and handheld positions. Each video was processed using a trained neural network that detected 2D anatomical keypoints frame-by-frame. The dataset includes: Vertical Displacement Signal (VDS) values for eye, withers, and croup Reference groundline data (fixed and algorithm-estimated) Stride-level Maxdiff and Mindiff values This dataset enables the comparison of groundline estimation and stride symmetry metrics across different camera angles, including handheld usage. It may serve as a reference for future development and validation of markerless, portable gait analysis systems in veterinary medicine.