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

• URL: http://arxiv.org/abs/2609.02705v1
• Date: Wed, 02 Sep 2026 15:07:24 GMT
• Title: A Top-Down Framework for Metric-Scale Athlete Localization from Single Broadcast Frames
• Abstract summary: We propose a top-down framework for metric-scale athlete localization from a single frame.<n> Boundary-Aware Adaptive Tiling mitigates boundary-splitting artifacts.<n>Second, we adapt the RTMPose-X architecture into a specialized two-keypoint estimator.<n>Third, we deterministically lift the 2D ground projections into world coordinates via camera-calibrated ray casting.
• Score: 8.167255229937995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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