ArticleScientific reports2026
A smart monocular vision metrology system based on computer for standing long jump.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The standing long jump (SLJ) is widely used for large-scale fitness assessment, yet existing distance measurement solutions remain labor-intensive or hardware-dependent. We frame SLJ distance estimation as a markerless monocular imaging-metrology problem and present a reusable vision pipeline comprising key-frame selection, person detection, semantic segmentation of the heel and jump-mat, homography-based plane mapping, and distance computation with polynomial compensation for perspective-edge bias. Field tests with a high-frame-rate camera demonstrate real-time performance (≈ 23 FPS, ~ 42.7 ms per frame) and centimeter-level accuracy. Using manual tape measurement as the reference, the overall system attains an MAE of 0.71 cm; ablations show that removing key modules sharply degrades accuracy (e.g., 25.07 cm without incomplete-athlete handling; 4.35 cm without single-view perspective mapping; 2.31 cm without convex-hull/curvature-based heel extraction). Minimal calibration files and inference scripts are provided to support reproducibility and deployment in school testing.
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