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Comparative Analysis of Machine Learning and Deep Learning Models for Real-Time Soccer Dribbling Technique Classification Using Video-Based Pose Estimation

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2025, 30(9), pp.43~52
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 28, 2025
  • Accepted : September 4, 2025
  • Published : September 30, 2025

Wansuk Choi 1 Liao Liang 1 Hye Young Kwon 1 Hyung Soo Shin 1 Wei Hanyi 2 TaeSeok Choi 3 Seoyoon Heo 4 Myeong-Chul Park 1

1경운대학교
2경북대학교
3군장대학교
4경복대학교

Accredited

ABSTRACT

This paper proposes machine learning models for classifying five soccer dribbling techniques using video-based pose estimation. We collected 48 videos of dribbling techniques (feinting, inside-outside, flip flap, Ronaldo chop, step over), extracted 3D coordinates of 33 anatomical landmarks from 21,186 frames using MediaPipe Pose, and compared six models through 5-fold cross-validation. XGBoost achieved the highest accuracy (99.11%), followed by Random Forest (98.94%), while deep learning models showed lower performance: 1D CNN (97.64%), LSTM (97.31%), Transformer (88.03%), and GRU-CNN (86.66%). All models exceeded real-time requirements with XGBoost reaching 203,484 FPS. Feature importance analysis revealed lower body landmarks contributed 68% to classification decisions. Results demonstrate tree-based ensemble methods outperform deep learning for dribbling classification by effectively utilizing spatial coordinate relationships, enabling real-time, high-accuracy analysis for training applications.

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