A Method for Table Tennis Bat Trajectories Reconstruction with the Fusion of Human Keypoint Information

Tracing and restoring the bat trajectory from a table tennis match video plays a critical role in table tennis technique and tactics. However, directly detecting the bat location in each frame is challenging, because the bat shapes are changed be-cause of various movement speeds, and can sometimes be am-biguous. In addition, there are rare bat-related datasets. In this paper, we propose a novel method that decomposes the prob-lem into two stages. In the first stage, we employ YOLOv5 for bat detection in each frame, and filter out erroneous candidate boxes. In the second stage, we classify all frames into two cat-egories. For frames corresponding to forward swing motions, we utilize a temporal prediction model that integrates human body keypoints and confidence scores to fill missing values. For other frames, missing values are completed using inter-polation methods. By combining these two results, we obtain a complete tennis bat trajectory with reduced errors in trajec-tory reconstruction. To evaluate the effectiveness of the pro-posed method, we build a video dataset that with labeled bat locations in each frame. The evaluation results demonstrate that the proposed method outperforms traditional methods on precision performance metric.