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Related Experiment Videos

Scene Classification for Sports Video Summarization Using Transfer Learning.

Muhammad Rafiq1, Ghazala Rafiq1, Rockson Agyeman1

  • 1Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Korea.

Sensors (Basel, Switzerland)
|March 22, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for sports video scene classification, achieving 99.26% accuracy in cricket video summarization using AlexNet Convolutional Neural Network (CNN). The novel approach enhances automated video summarization efficiency.

Keywords:
AlexNet CNNdata augmentationdeep learningsmall dataset

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Automated video summarization is crucial for marketing and content creation, but requires accurate scene classification.
  • Existing sports video scene classification methods have practical implementation gaps.
  • High-quality scene classification is fundamental for effective video summarization.

Purpose of the Study:

  • To propose a novel method for sports video scene classification specifically for video summarization.
  • To address practical implementation gaps in existing scene classification techniques.
  • To achieve high-quality scene classification for automated video summarization.

Main Methods:

  • A novel method using a pre-trained AlexNet Convolutional Neural Network (CNN) for scene classification.
  • Incorporation of new, fully connected layers in an encoder fashion within the CNN architecture.
  • Application of data augmentation techniques to improve classification accuracy.

Main Results:

  • Achieved a high accuracy of 99.26% for cricket video scene classification on a smaller dataset.
  • Demonstrated superior performance compared to baseline approaches and state-of-the-art models.
  • The proposed method with AlexNet CNN outperformed other deep learning models like Inception V3, VGGNet, and ResNet50.

Conclusions:

  • The proposed method offers a significant improvement in sports video scene classification accuracy.
  • This advancement facilitates more efficient and accurate automated video summarization.
  • The method shows strong potential for various applications requiring video analysis and content summarization.