Sports Videos Classification using Advanced Deep Neural Networks

G. Syam Kumar

doi:10.62760/iteecs.3.2.2024.92

Authors

G. Syam Kumar Department of Physical Education, Jawaharlal Nehru Technological University Kakinada, Kakinada-533003, India

DOI:

https://doi.org/10.62760/iteecs.3.2.2024.92

Keywords:

Sports video, Convolution neural networks, Deep learning, Maximum mean difference

Abstract

The field of digital content is experiencing a meteoric rise in popularity as a direct result of the rapid development of information technology. When it comes to the archiving of digital content on the assistant, the segregation in sports videos is of an extremely important part. Consequently, the utilization of deep-neural-network algorithm (DNN), convolutional-neural-network (CNN), and deliver learning allows for the correct segregation of sports video classification to be achieved. There are two methods that have been proposed: block-brightness-comparison-coding (BICC) cum block colour histogram. Both of these methods analyze the contrast relationship among various parts of a video cum the colour matter that is present in a sector. In order to accomplish the goal of transfer learning, the maximum-mean-difference (MMD) procedure is utilized. Obtaining characteristics in sports video pictures is the foundation for the sports video image segregation approach that is dependent on deep-learning-coding model. This method is utilized in order that accomplish task of sports video segregation. As a consequence of the findings, it is clear that the overall segregation reaction of this procedure is significantly superior to that of other sports video classification methods that are currently in use. This results in a significant improvement in the classification effect of sports videos.

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