  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.
