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Image Semantic Segmentation Approach for Studying Human Behavior on Image Data

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Image semantic segmentation is an essential technique for studying human behavior through image data.This paper proposes an image semantic segmentation method for human behavior research.Firstly,an end-to-end convolutional neural network architecture is proposed,which consists of a depth-separable jump-connected fully convolutional network and a conditional random field network;then jump-connected convolution is used to classify each pixel in the image,and an image semantic segmentation method based on convolu-tional neural network is proposed;and then a conditional random field network is used to improve the effect of image segmentation of hu-man behavior and a linear modeling and nonlinear modeling method based on the semantic segmentation of conditional random field im-age is proposed.Finally,using the proposed image segmentation network,the input entrepreneurial image data is semantically segmented to obtain the contour features of the person;and the segmentation of the images in the medical field.The experimental results show that the image semantic segmentation method is effective.It is a new way to use image data to study human behavior and can be extended to other research areas.

human behavior researchimage semantic segmentationhop-connected full convolution networkconditional random field networkdeep learning

ZHENG Zhan、CHEN Da、HUANG Yanrong

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School of Communication,Wuhan Textile University,Wuhan 430073,Hubei,China

Walnut Street(Shanghai)Information Technology Co.,Ltd.,Shanghai 200051,China

College of Economics & Management,Zhejiang University of Water Resources and Electric Power,Hangzhou 310018,Zhejiang,China

Research Center for Digital Economy and Sustainable Development of Water Resources,Hangzhou 310018,Zhejiang,China

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Major Consulting and Research Project of the Chinese Academy of EngineeringNational Natural Science Foundation of ChinaZhejiang Soft Science Research Program

2020-CQ-ZD-l721012352023C35012

2024

10.1051/wujns/2024292145

武汉大学自然科学学报(英文版)
武汉大学

武汉大学自然科学学报(英文版)

CSTPCD
影响因子：0.066
ISSN：1007-1202
年,卷(期)：2024.29(2)
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