用普通视频自动分析网球击球动作，还能评姿势和给建议。
Multi-Task Tennis Stroke Biomechanics Analysis Using MediaPipe Pose
- 基于姿态关键点，用Transformer模型同时识别击球类型、方向和姿势质量。
- 跨选手测试显示击球类型识别准确率82.9%，但方向预测无法迁移。
- 使用世界坐标系关键点显著提升泛化能力，图像坐标导致准确率暴跌。
我们构建了一个从普通RGB视频中进行多任务网球击球生物力学分析的管道。在基于姿态的击球识别基础上，新增击球方向预测与姿势质量评分任务，并加入规则反馈层提供训练建议。击球通过加权关节速度分数自动检测：s(t) = 0.5 v_wrist + 0.3 m_elbow + 0.2 m_shoulder，无需人工标注。姿态数据来自MediaPipe Pose Landmarker（33个关键点，世界坐标），每段击球转为30帧×39特征序列输入TennisTransformerGPU——一个仅564,103参数的轻量Transformer（4层，4头，d=128），含三个并行输出头。在7位职业选手和1位业余选手共11段视频中的1,281段标记击球上训练，击球类型准确率达83.7%，方向预测61.9%，姿势评分62.6%（随机80/20划分）。跨选手测试中，以职业选手训练、业余选手测试，击球类型准确率降至82.9%（下降0.8%），方向预测则退化至多数类基准。消融实验表明世界坐标至关重要：切换至图像空间坐标后，击球类型准确率从83%跌至47%，方向从68%降至21%。所有流程可在Kaggle免费T4 GPU上运行，完全可复现。
原文摘要 · Abstract (English)
We built a multi-task pipeline for tennis stroke biomechanics from plain RGB video. On top of pose-based stroke recognition, it adds two new tasks, predicting shot direction and grading posture quality, plus a rule-based feedback layer that suggests coaching tips. Strokes are found automatically using a weighted joint velocity score, s(t) = 0.5 v_wrist + 0.3 m_elbow + 0.2 m_shoulder, removing the need for manual annotation. Pose comes from MediaPipe Pose Landmarker (33 landmarks, metric world coordinates), with each stroke turned into a 30-frame by 39-feature sequence for TennisTransformerGPU, a compact 564,103-parameter transformer (4 layers, 4 heads, d=128) with three parallel output heads. Trained on 1,281 labeled strokes from 7 pros and 1 amateur across 11 videos, it hits 83.7% stroke-type accuracy, 61.9% on direction, and 62.6% on posture under a random 80/20 split. The interesting test is cross-player: train on pros, evaluate on the amateur. Stroke type barely budges, 82.9%, a 0.8% drop. Direction prediction does not transfer; it just falls back to the majority class. An ablation shows why world coordinates matter so much here: switching to image-space landmarks tanks cross-player stroke-type accuracy from 83% to 47% and direction from 68% to 21%. Everything runs on Kaggle's free T4 GPU tier and is fully reproducible.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。