A Top-Down Framework for Metric-Scale Athlete Localization from Single Broadcast Frames

2026-09-02

AI Summary

本文提出了一种自顶向下的框架，通过单帧广播画面实现运动员的准确世界坐标定位，主要利用边界感知自适应切片和改进的RTMPose-X架构解决尺度变化和透视失真问题。

Abstract

Accurate world-coordinate localization of athletes from single-frame broadcast footage is inherently challenging due to extreme scale disparities in ultra-high-resolution imagery. In this paper, we propose a top-down framework for metric-scale athlete localization from a single calibrated frame. Our approach centers on three key contributions. First, we propose Boundary-Aware Adaptive Tiling, a semantics-guided extension of standard sliced inference. By iteratively expanding tile boundaries based on coarse bounding-box predictions, it systematically ensures full object containment, effectively mitigating boundary-splitting artifacts through a lightweight pipeline adaptation without architectural modifications. By substantially mitigating recall degradation under extreme scale variance, Boundary-Aware Adaptive Tiling enables us to isolate perspective distortion as the primary source of residual localization error. Second, we adapt the RTMPose-X architecture into a specialized two-keypoint estimator (pelvis and ground projection), employing a reformulated Gated Attention Unit optimized for this geometrically coupled point pair, and then deterministically lift the 2D ground projections into world coordinates via camera-calibrated ray casting. On the public test set, our method achieves a LocSim score of 97.44 and an mAP of 0.9128, outperforming the baseline by over 21 \% and establishing a robust solution for high-resolution scale variance.

Problem

• Athlete Localization
• Single Frame
• Ultra-High-Resolution
• World Coordinates
• Scale Disparities

Innovation

• Boundary-Aware Adaptive Tiling
• RTMPose-X
• Gated Attention Unit
• Camera-calibrated Ray Casting