基于图像差分的CNN运动分类

Wafaa Ahmed, A. Karim
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引用次数: 5

摘要

人类行为的分类已成为近年来研究的一个重要课题。通常,将识别人类动作的功能转换为对代表人体运动的图像进行分类的功能。为了对人体运动进行分类,本文采用卷积神经网络(convolutional Neural Network, CNN)通过卷积层提取特征,在全连通层中使用Softmax分类器对运动进行分类。该方法评估两个序列帧之间的差异,并将该帧差异用于CNN的训练和测试。该系统已在KTH、Ixmas和Weizmann三个数据库中得到应用。实验结果KTH的准确率为98.75%,Ixmas的准确率为92.24%,Weizmann数据库的准确率为100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Motion Classification Using CNN Based on Image Difference
The classification of human actions has become an important topics in recent researches. Typically the function of recognition human action is converted to the function of classifying the image that represents the person’s motion. In this paper to classify the human motion the Convolution Neural Network (CNN) has been used to extract features by convolutional layers and in fully connected layer Softmax classifier is used to classify the motion. This method evaluate the differences between two sequences frames and this frame differences is used for training and testing in CNN. The propose system has been applied on three databases KTH, Ixmas and Weizmann. The results of experiments achieved accuracy 98.75% with KTH, 92.24% with Ixmas and 100% with Weizmann database.
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