基于神经网络的视频分类教育

R. Vrskova, R. Hudec, P. Sykora, P. Kamencay, M. Radilova
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引用次数: 1

摘要

本文提出了一种用于视频分类的人工神经网络。人工神经网络是视频分类的重要组成部分。随着它们越来越多地用于视频分类的应用,将它们引入到教学过程中是很有必要的。在介绍了该主题之后,继续介绍了用于视频分类的结构神经网络的基本理论。首先,应用基于ucf50动作识别库的三维卷积神经网络(3DCNN);然后在同一数据集上使用卷积长短期记忆(ConvLSTM)。最后,对这些使用混淆矩阵的神经网络进行了比较。所有实验结果均采用ucf50数据集进行。实验结果证明了神经网络(3D CNN和ConvLSTM)在教育过程中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Education of Video Classification Based by Neural Networks
In this paper an artificial neural network for video classification were presented. Artificial neural networks were essential part of the video classification. As they are more and more used for applications doing of video classification, it is desirable to introduce them in the educational process. After introduction to the topic, basic theory about architecture neural networks for video classification continues. Firstly, the 3D Convolution Neural network (3DCNN) using UCF50-action recognition database was applied. Next the Convolutional Long Short-Term Memory (ConvLSTM) on the same dataset was used. Finally, these neural networks using confusion matrix were compared. The all experimental results using UCF50-Datatset were performed. The achieved experimental results demonstrate the effectiveness of neural networks (3D CNN and ConvLSTM) in educational process.
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