利用關節點聯合體積空間三角測量和棒球客製化濾波 器系統重建三維棒球投手姿態

Abstract

三維人體重建在棒球分析中的重要性與日俱增，但是在現實世界中的棒球投球姿態預測仍有不少困難需要克服。首先，野外的棒球投球姿勢缺少相關三維人體影像資料集，並且存在許多被身體部位遮蔽的關節點；其次，棒球投球動作在上肢加速期時關節點存在劇烈的速度變化。由於這個原因，用一般濾波器來去除隨機雜訊，同時保留投球運動的高頻訊號是非常困難的。為了解決前面所述的問題，我們提出了「關節點聯合體積空間三角測量」，透過利用各角度二維關節點熱力圖的資訊，去得到更為精準的三維人體姿態重建結果。我們另外設計了棒球客製化的濾波器系統以去除投球運動中的雜訊，同時保留投球的高頻運動信號。我們所提出的姿態重建方式在棒球投球相關動作中可以達到 33.1 毫米的平均位置誤差和 0.35 米/秒 (1.28 公里/小時) 的平均速度誤差。我們的研究成果可以直接使用在室內環境或真實棒球場上的人體姿態重建。

3D human pose estimation (HPE) has become increasingly important in baseball analytic, but there are several difficulties pertaining to pose estimation in real-world baseball pitching. First, in-the-wild baseball pitching lacks related 3D pose datasets and contains lots of joints occluded by other body parts. Second, baseball pitching contains dramatic velocity changes during arm acceleration phases. Due to these properties of pitching, it is difficult to use common filters to remove random noises while preserving high-frequency critical joint movements in pitching. To solve these problems, we propose joint-wise volumetric triangulation to reconstruct 3D human poses by utilizing the information of multiview 2D joint heatmaps generated by 2D HPE methods. We also designed a baseball customized filter system to remove noisy signal from pose movement while preserving the high-frequency pitching motion. Our proposed pose reconstruction scheme yields a 33.1 mm average position error and 0.35m/s (1.28 km/h) average velocity error on baseball pitching motion. Our work can be directly applied to estimate human poses either in indoor environment or real-world baseball field.