SOURCE-LINKED INTELLIGENCE
A Top-Down Framework for Metric-Scale Athlete Localization from Single Broadcast Frames
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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-02T15:07:24.000Z
First collected: 2026-09-21T05:32:15.665Z. This is not the publication date.