This study focuses on developing a novel system to improve the performance of short-distance races, where stride length, stride frequency, and maximum velocity are important factors. To estimate stride length and stride frequency, color-based image processing is adopted to extract the feet of a runner, based on cosine similarity in the RGB color space. The experimental results indicate that the stride length and stride frequency could be estimated with negligible errors. To estimate the running velocity; visual object detection, and pose estimation based on state-of-the-art deep learning schemes were applied: RetinaNet for visual object detection, and OpenPose for pose estimation. The experimental results using the real image dataset, indicated that the estimation error of the velocity by the proposed scheme was quite negligible.
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