Enhanced Pose Estimation for Badminton Players via Improved YOLOv8-Pose with Efficient Local Attention

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 14; p. 4446
• Main Authors: Wu, Yijian, Chen, Zewen, Zhang, Hongxing, Yang, Yulin, Yi, Weichao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.07.2025; MDPI
• Subjects: Accuracy; Algorithms; Analysis; Annotations; Artificial Intelligence; Attention - physiology; Badminton; Badminton (Game); badminton scene; Datasets; Deep learning; efficient local attention; human pose estimation; Humans; Localization; Movement - physiology; Neural networks; Posture - physiology; Racquet Sports - physiology; sport analytics; YOLOv8-Pose; Germany; sport analytics; YOLOv8-Pose; badminton scene; human pose estimation; efficient local attention

With the rapid development of sports analytics and artificial intelligence, accurate human pose estimation in badminton is becoming increasingly important. However, challenges such as the lack of domain-specific datasets and the complexity of athletes’ movements continue to hinder progress in this a...