Jump-Aware: Player Position Rectification and Identification in Dynamic Sports Using Jump Event Spotting

Abstract

Accurate player positioning and identification in broadcast sports videos are essential for sports analytics. However, jump-intensive sports such as basketball and volleyball pose significant challenges due to positional distortions caused by airborne motion and occlusions. To address these issues, we introduce Jump-Aware Position Rectification (JPR), a framework that integrates Jump Event Spotting (JES) and jersey-based player identification to improve spatial consistency and identity tracking. Our method first detects and validates jump events, then rectifies player positions in a top-view pitch coordinate system, reducing motion artifacts caused by temporary elevation changes in 2D image space. Additionally, jerseybased identification enhances identity tracking by leveraging jersey numbers, even under occlusions. To support our research, we present SportsJumpMotion, a dataset featuring frame-accurate jump annotations and jersey-based player identities for basketball and volleyball. Experimental results demonstrate that our JES model achieves a mean Average Precision (mAP) of 95.33, outperforming baseline models in jump event spotting. Furthermore, fine-tuning on sport-specific datasets significantly improves jersey-based identification, addressing variations in jersey visibility and motion patterns across sports. Our dataset and framework provide a comprehensive benchmark for advancing player tracking in dynamic sports scenarios. Our SportsJumpMotion dataset is publicly available at https://github.com/yinmayoo185/SportsJumpMotion.