Sports video classification using convolutional neural network (CNN) with normalization flow.

Emran, Nurul Akmar and Md Saleh, Nurul Izrin and Mohd Ali, Muhammad Zaidi (2024) Sports video classification using convolutional neural network (CNN) with normalization flow. In: 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS).

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Abstract

Classifying sports videos is a crucial task in various applications, especially sports analytics, video retrieval, and content analysis. Unlike image classification, video classification is more challenging due to the dynamic nature of videos. Advancements in deep learning, particularly Convolutional Neural Network (CNN), have shown promising results in sports video classification. In this paper, we present the results of implementing CNN with normalization flow to classify several sports classes. In particular, the effect of classification accuracy in training datasets by increasing the number of classes (with similar characteristics and some noise) is analyzed. The effect of frame averaging, and the number of epochs were also observed. The results show that for the training dataset, CNN performance is slightly affected by the additional classes, but by increasing the number of epochs, the accuracy of training and validation datasets has improved. CNN can still maintain highly accurate classifications in test datasets (more than 80%) in some observations. CNN with frame averaging shows lower errors than single-frame CNN.

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Information and Communication Technology
Depositing User:	NURHASHIRAH BORHAN
Date Deposited:	31 Jul 2026 03:57
Last Modified:	31 Jul 2026 03:57
URI:	http://eprints.utem.edu.my/id/eprint/30091
Statistic Details:	View Download Statistic

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