In the high-stakes world of baseball, every nuance of a pitcher's mechanics holds the key to maximizing performance and minimizing runs. Traditional analysis methods often rely on pre-recorded offline numerical data, hindering their application in the dynamic environment of live games. Broadcast video analysis, while seemingly ideal, faces significant challenges due to factors like motion blur and low resolution. To address these challenges, we introduce PitcherNet, an end-to-end automated system that analyzes pitcher kinematics directly from live broadcast video, thereby extracting valuable pitch statistics including velocity, release point, pitch position, and release extension. This system leverages three key components: (1) Player tracking and identification by decoupling actions from player kinematics; (2) Distribution and depth-aware 3D human modeling; and (3) Kinematic-driven pitch statistics. Experimental validation demonstrates that PitcherNet achieves robust analysis results with 96.82% accuracy in pitcher tracklet identification, reduced joint position error by 1.8mm and superior analytics compared to baseline methods. By enabling performance-critical kinematic analysis from broadcast video, PitcherNet paves the way for the future of baseball analytics by optimizing pitching strategies, preventing injuries, and unlocking a deeper understanding of pitcher mechanics, forever transforming the game.

翻译：在高度竞争的棒球运动中，投手动作机制的每一处细微差异都关乎最大化表现与最小化失分。传统分析方法通常依赖预录的离线数值数据，难以应用于比赛直播的动态环境。广播视频分析虽看似理想，却因运动模糊和低分辨率等因素面临巨大挑战。为应对这些问题，我们提出PitcherNet——一个直接从直播视频中分析投手运动学的端到端自动化系统，从而提取包括球速、释放点、投球位置及释放延伸在内的宝贵投球统计量。该系统利用三个关键组件：（1）通过解耦动作与投手运动学实现球员追踪与识别；（2）基于分布与深度感知的三维人体建模；（3）运动学驱动的投球统计量计算。实验验证表明，PitcherNet在投手轨迹识别中达到96.82%的准确率，关节位置误差减少1.8毫米，且分析性能优于基线方法。通过从广播视频实现关键性能驱动型运动学分析，PitcherNet为优化投球策略、预防伤病并深入理解投手机制开辟了棒球分析的新未来，从而永远改变这项运动。