Harness racing horses are exposed to high workload and consequently, they are at risk of joint injuries and lameness. In recent years, the interest in applications to improve animal welfare has increased and there is a demand for objective assessment methods that can enable early and robust diagnosis of injuries. In this thesis, experiments were conducted on video recordings collected by a helmet camera mounted on the driver of a sulky. The aim was to take the first steps toward equine gait analysis by investigating how semantic segmentation and 3D reconstruction of such data could be performed. Since these were the first experiments made on this data, no expectations of the results existed in advance. Manual pixel-wise annotations were created on a small set of extracted frames and a deep learning model for semantic segmentation was trained to localize the horse, as well as the sulky and reins. The results are promising and could probably be further improved by expanding the annotated dataset and using a larger image resolution. Structure-from-motion using COLMAP was performed to estimate the camera motion in part of a video recording. A method to filter out dynamic objects based on masks created from predicted segmentation maps was investigated and the results showed that the reconstruction was part-wise successful, but struggled when dynamic objects were not filtered out and when the equipage was moving at high speed along a straight stretch. Overall the results are promising, but further development needs to be conducted to ensure robustness and conclude whether data collected by the investigated helmet camera configuration is suitable for equine gait analysis.