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Human pose estimation of ski jumpers based on video

Ski jumping is a fast and wide-range motion. Although wearable devices are available to analyze the motion, it is cumbersome and difficult to implement. Since video data is relatively simple to obtain, this paper proposes a video-based method for estimating the pose of ski jumpers. In this method, we use a high-speed camera as a video data collector, and use Simi Motion software to convert the video into frames and manually annotate keypoints. The video data of three athletes is used to build the training set, and another is used to build the test set. In addition, we use High-Resolution Net (HRNet) to transfer the learning of feature knowledge from the public dataset COCO2017 to the task of ski jumpers pose estimation. The experiments show that under the real labeled bounding box, an average precision of 84.6% is obtained, which is higher than other mainstream human pose estimate methods.