论文标题
在四肢和滑雪板上检测任意关键点，稀疏的部分正确的分割掩模
Detecting Arbitrary Keypoints on Limbs and Skis with Sparse Partly Correct Segmentation Masks
论文作者
论文摘要
基于身体姿势的分析对于许多运动学科中的顶级运动员至关重要。如果有的话，教练仅标记最重要的关键点，因为手动注释非常昂贵。本文提出了一种在专业滑雪跳线的四肢和滑雪板上检测任意关键的方法，该方法需要一些，只需要在训练过程中部分纠正分段掩码。我们的模型基于视觉变压器体系结构，其特殊设计用于输入令牌以查询所需的关键。由于我们仅使用分割掩码来生成可自由选择的关键点的地面真实标签，因此部分正确的分段掩码足以使我们的培训程序足以进行。因此，不需要昂贵的手工注销面具。我们为包括伪标签在内的自由选择和标准关键点分析了不同的训练技术，并在我们的实验中显示，只有少数部分正确的分割掩码足以学习检测四肢和滑雪板上的任意关键点。
Analyses based on the body posture are crucial for top-class athletes in many sports disciplines. If at all, coaches label only the most important keypoints, since manual annotations are very costly. This paper proposes a method to detect arbitrary keypoints on the limbs and skis of professional ski jumpers that requires a few, only partly correct segmentation masks during training. Our model is based on the Vision Transformer architecture with a special design for the input tokens to query for the desired keypoints. Since we use segmentation masks only to generate ground truth labels for the freely selectable keypoints, partly correct segmentation masks are sufficient for our training procedure. Hence, there is no need for costly hand-annotated segmentation masks. We analyze different training techniques for freely selected and standard keypoints, including pseudo labels, and show in our experiments that only a few partly correct segmentation masks are sufficient for learning to detect arbitrary keypoints on limbs and skis.
