Video Shot Boundary Detection and Sports Video Classification Algorithm Based on Particle Filter

Authors

  • Dongsheng Chen Guangxi College for Preschool Education
  • Zhen Ni Nanning Normal University image/svg+xml

DOI:

https://doi.org/10.4108/eetsis.3955

Keywords:

Deep learning, Particle filter, Sports video, Categorize, Edge detection, Key frame, Encoding mode

Abstract

INTRODUCTION: Sports video is a very important information resource. The classification of sports video with high accuracy can effectively improve the browsing and query effect of users. This project intends to study a motion video classification algorithm based on deep learning particle filter to solve the problems of strong subjectivity and low accuracy of existing motion video classification algorithms. A key box extraction method based on similarity is proposed. The moving video classification algorithm based on deep learning coding model is studied. Examples of various types of sports videos are analyzed. The overall performance of the motion video classification algorithm proposed in this paper is much better than other existing motion video classification algorithms. This algorithm can greatly improve the classification performance of motion video.

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Published

31-10-2023

How to Cite

1.
Chen D, Ni Z. Video Shot Boundary Detection and Sports Video Classification Algorithm Based on Particle Filter. EAI Endorsed Scal Inf Syst [Internet]. 2023 Oct. 31 [cited 2024 Jun. 23];11(3). Available from: https://publications.eai.eu/index.php/sis/article/view/3955