用球轨迹和骨骼数据提升羽毛球击球类型识别准确率
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports
- 基于发球动作剪裁视频，融合骨骼与球轨迹信息
- 在三个数据集上超越现有最优模型，最高提升4.2%准确率
- 适合研究网球/羽毛球等球类运动动作识别的学者
羽毛球是所有体育项目中球速最快的，给计算机视觉带来挑战，包括球员识别、球场线检测、球飞行轨迹追踪以及球员击球类型分类。本文提出一种新的视频片段提取策略，从羽毛球比赛转播中截取每位球员击球时的帧序列。这些帧经由三个现成模型处理：人体姿态估计获取人体骨骼关节点，球轨迹追踪模型预测球的运动路径，球场线检测模型确定球员场上位置。将上述信息作为输入，我们提出羽毛球击球类型变换器（BST），用于单打比赛中的击球类型分类。据我们所知，实验结果表明，该方法在目前最大的公开羽毛球视频数据集ShuttleSet、另一个羽毛球数据集BadmintonDB以及一个网球数据集TenniSet上均优于此前的最先进方法。这些结果表明，有效利用球的运动轨迹是提升球类运动动作识别性能的重要方向。
原文摘要 · Abstract (English)
Badminton, known for having the fastest ball speeds among all sports, presents significant challenges to the field of computer vision, including player identification, court line detection, shuttlecock trajectory tracking, and player stroke-type classification. In this paper, we introduce a novel video clipping strategy to extract frames of each player's racket swing in a badminton broadcast match. These clipped frames are then processed by three existing models: one for Human Pose Estimation to obtain human skeletal joints, another for shuttlecock trajectory tracking, and the other for court line detection to determine player positions on the court. Leveraging these data as inputs, we propose Badminton Stroke-type Transformer (BST) to classify player stroke-types in singles. To the best of our knowledge, experimental results demonstrate that our method outperforms the previous state-of-the-art on the largest publicly available badminton video dataset (ShuttleSet), another badminton dataset (BadmintonDB), and a tennis dataset (TenniSet). These results suggest that effectively leveraging ball trajectory is a promising direction for action recognition in racket sports.
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