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Intelligent Sports Video Classification Based on Deep Neural Network (DNN) Algorithm and Transfer Learning.

Xiaoping Guo1

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This study introduces novel methods for sports video retrieval, enhancing accuracy and efficiency. The developed algorithms improve key frame extraction and keyword finding, significantly boosting video search performance.

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Multimedia Systems

Background:

  • Traditional video retrieval methods (text annotation, content-based) are inefficient, subjective, or limited in accurately describing video content.
  • Existing content-based retrieval often relies on convolutional neural networks and similarity algorithms, which may not fully capture the nuances of sports videos.

Purpose of the Study:

  • To develop advanced techniques for sports video retrieval that overcome the limitations of traditional methods.
  • To improve the accuracy and efficiency of video shot boundary detection, key frame extraction, and keyword-based retrieval.

Main Methods:

  • Proposed a histogram difference method with transfer learning for mutation detection and a four-step block matching method for fading detection in sports videos.
  • Implemented adaptive thresholding for candidate shot region identification and mutation detection for precise shot boundary determination.
  • Developed a key frame extraction algorithm integrating clustering and optical flow analysis, outperforming traditional clustering methods.
  • Introduced an improved deep neural network and ontology semantic expansion for fuzzy keyword finding.

Main Results:

  • The proposed shot detection methods effectively identify mutation and fading, accurately determining shot boundaries.
  • The clustering and optical flow-based key frame extraction algorithm successfully removes redundant frames, yielding more representative key frames.
  • Extensive experiments demonstrated superior retrieval performance with the improved deep neural network and ontology semantic expansion for keyword fuzzy finding.
  • The system achieved reduced false detection and leakage rates while improving fidelity in large-scale internet video resource retrieval.

Conclusions:

  • The developed methods are feasible for video underlying feature extraction, annotation, and keyword finding, particularly for sports videos.
  • The proposed algorithms offer a significant improvement in retrieving desired videos from large datasets, meeting daily user needs.
  • This research contributes to more effective and efficient video retrieval systems, especially in the domain of sports content.