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Related Experiment Video

Updated: Jul 1, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Football referee gesture recognition algorithm based on YOLOv8s.

Zhiyuan Yang1, Yuanyuan Shen1, Yanfei Shen1

  • 1School of Sport Engineering, Beijing Sport University, Beijing, China.

Frontiers in Computational Neuroscience
|March 5, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced deep learning model for football referee gesture recognition (FRGR), improving accuracy in complex match environments. The optimized model significantly outperforms existing methods for automated gesture interpretation.

Keywords:
GAMMDPIoUP2 detection headYOLOv8sdeep learningfootball gesture recognition

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Sports Technology

Background:

  • Automated football referee gesture recognition (FRGR) is challenging due to diverse gestures and environmental interference.
  • Existing visual sensor methods often yield unsatisfactory performance in FRGR tasks.

Purpose of the Study:

  • To develop an improved deep learning model for accurate football referee gesture recognition.
  • To address limitations in current FRGR methods using novel optimization strategies.

Main Methods:

  • A deep learning model based on YOLOv8s was developed, incorporating a Global Attention Mechanism (GAM) for focused attention on gestures.
  • Integration of a P2 detection head for enhanced small object detection and a Minimum Point Distance Intersection over Union (MPDIoU) loss function were employed.
  • Experiments were conducted on a dataset of 1,200 images featuring six distinct referee hand gestures.

Main Results:

  • The proposed model achieved a precision of 89.3%, recall of 88.9%, mAP@0.5 of 89.9%, and mAP@0.5:0.95 of 77.3%.
  • Performance improvements of 1.4% (precision), 2.0% (recall), 1.1% (mAP@0.5), and 5.4% (mAP@0.5:0.95) over the latest YOLOv8s were observed.
  • The model demonstrated superior performance compared to seven existing models and 10 optimization variants.

Conclusions:

  • The developed deep learning model offers a promising solution for automated football referee gesture recognition.
  • The integration of GAM, P2 detection head, and MPDIoU loss function effectively enhances FRGR accuracy.
  • The method shows significant potential for practical application in improving communication and officiating in football matches.