Communication Dans Un Congrès Année : 2024

Deep Learning for Automated Localization of Gaze Points and Climbing Holds

William Pantry
  • Fonction : Auteur
Ludovic Seifert
Alexandre Perier
  • Fonction : Auteur

Résumé

Points of gaze (PoGs) and motor behaviors impact sport climbing performance. A large dataset of global PoGs and climbing holds (CHs) is needed. Recent eye-tracking devices capture only local views, leading to time-consuming global localization. This study aims to automate global PoG and CH computation. A wireless eye-tracking device records PoGs and CHs during climbs. Artificial landmarks aid in mapping to global space. A CNN-based framework detects and classifies landmarks. Local PoGs and CHs are transformed globally using a homography transform. Cross-validation assessed the method's success rates and accuracies. The optimal framework computed global PoGs and CHs for 2,460 climbing cases. CH success rates were 80.90% ± 13.98%, with mean Euclidean distance errors of 0.0239 ± 0.0216 m. PoG success rates were 80.79% ± 10.74%. Processing time per frame averaged 115.14 ± 6.80 ms. The datasets will analyze gaze behaviors' effects on climbing outcomes and inform a decision-support system for sport climbing.

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Dates et versions

hal-05110360 , version 1 (12-06-2025)

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Citer

William Pantry, Ludovic Seifert, Guillaume Hacques, Alexandre Perier, Youssef Chahir. Deep Learning for Automated Localization of Gaze Points and Climbing Holds. 2024 IEEE Thirteenth International Conference on Image Processing Theory, Tools and Applications (IPTA), Oct 2024, Rabat, France. pp.01-06, ⟨10.1109/IPTA62886.2024.10755884⟩. ⟨hal-05110360⟩
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