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Automated Badminton Game Analysis: Integrating YOLO Models for Enhanced Performance Evaluation

  • Unique Paper ID: 169085
  • Volume: 11
  • Issue: 6
  • PageNo: 2499-2505
  • Abstract:
  • This paper introduces an AI-driven prototype about the analysis of gameplay in badminton, mainly featuring the detection and tracking of shuttlecock and positions of players. A deep learning model is proposed based on YOLO architecture which resulted in a strong program for real-time identification of player positions and the trajectory followed by a shuttlecock. This system made the shuttlecock detection on the YOLO model customized by using the pre-trained model for player detection. The system adds a bounding box to the detected players and shuttlecock and maps their position from the frame relative to the court. Then, using a mini-court representation, it visualizes player movements and shuttlecock trajectories. The proposed system has experimental results with high accuracy on shuttlecock and player detection under different game conditions translated into useful information for the evaluation of player performance. The framework added value to sports analytics by providing automated analysis of badminton matches, potentially informing improved strategies for coaches and player training.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 6
  • PageNo: 2499-2505

Automated Badminton Game Analysis: Integrating YOLO Models for Enhanced Performance Evaluation

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