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Sports Video Classification Framework Using Enhanced Threshold Based Keyframe Selection Algorithm and Customized CNN

M Ramesh1, K Mahesh1

  • 1Department of Computer Applications, Alagappa University, Karaikudi, Tamil Nadu, India.

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Summary

This study introduces a deep learning framework for sports video classification. The model accurately identifies sports from videos, aiding athletes and trainers in performance analysis.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Sports Analytics

Background:

  • The computer vision field shows increasing interest in sports video analysis.
  • Existing video classification models lack specificity for sports-related content.
  • Advancements in sports technology necessitate specialized video analysis tools.

Purpose of the Study:

  • To propose a novel deep learning framework for accurate sports video classification.
  • To enable identification of specific sports from large video datasets for performance analysis.
  • To assist athletes and trainers in leveraging video data for improved performance.

Main Methods:

  • Developed a deep learning framework for sports video classification.
  • Implemented preprocessing steps including frame extraction and noise reduction.
  • Utilized keyframe selection via candidate frame extraction and a threshold-based frame difference algorithm.
  • Employed Convolutional Neural Networks (CNNs) for feature extraction and classification.
  • Validated the framework on UCF101 and Sports1-M benchmark datasets.

Main Results:

  • The proposed framework achieved accurate sports video classification.
  • Performance was benchmarked against pre-trained neural networks like AlexNet and GoogleNet.
  • Evaluation metrics demonstrated the framework's effectiveness and accuracy.

Conclusions:

  • The developed deep learning framework offers a robust solution for sports video classification.
  • This technology can significantly aid in sports performance analysis and training.
  • The framework provides a foundation for future research in specialized video recognition.