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Highlights

Stereo machine vision and deep learning techniques were investigated for infield equine kinematic gait analysis.

Abstract. Equine kinematic gait analysis (EKGA) currently requires a complicated, expensive, and labor-intensive procedure for equine locomotion research. An automated stereo video processing pipeline was developed and evaluated for measuring equine biomechanical parameters. Using stereo videos of 40 different walking horses, a DeepLabCut (DLC) model was trained to detect body landmarks in individual frames. With an autoregressive integrated moving average filter, the landmark detection had a root mean square error of 5.14 pixels and a mean absolute error of 4.87 pixels. As a case study, methods were developed to extract stride length (SL) and stance duration (SD). Individual hoof gait phase detection was achieved using a fine-tuned Faster R-CNN model and a mode filter, yielding precision and recall values of 0.83 and 0.95, respectively. The semi-global block matching (SGBM) algorithm was used to estimate depth maps, and the accuracy was assessed by comparing head length estimation with infield measurements. A Bland-Altman analysis for DLC-detected head length in combination with SGBM-based 3D reconstruction yielded a bias of -0.014 m with upper and lower limits of agreement (LoAs) of 0.03 m and -0.061 m, respectively. Furthermore, Bland-Altman analyses on SD and SL when compared to image-level manual measurements showed biases of -0.02 sec and -0.042 m, respectively. The corresponding LoAs were (0.01907 sec, -0.24 sec) for SD and (0.04 m, -0.12 m) for SL. The proposed method showed promising potential in performing EKGA in an automated, cost-effective, and rapid manner under field conditions.