Image Semantic Segmentation Approach for Studying Human Behavior on Image Data  

作　　者：ZHENG Zhan CHEN Da HUANG Yanrong 

机构地区：[1]School of Communication,Wuhan Textile University,Wuhan 430073,Hubei,China [2]Walnut Street(Shanghai)Information Technology Co.,Ltd.,Shanghai 200051,China [3]College of Economics&Management,Zhejiang University of Water Resources and Electric Power,Hangzhou 310018,Zhejiang,China [4]Research Center for Digital Economy and Sustainable Development of Water Resources,Hangzhou 310018,Zhejiang,China

出　　处：《Wuhan University Journal of Natural Sciences》2024年第2期145-153,共9页武汉大学学报（自然科学英文版）

基　　金：Supported by the Major Consulting and Research Project of the Chinese Academy of Engineering(2020-CQ-ZD-1);the National Natural Science Foundation of China(72101235);Zhejiang Soft Science Research Program(2023C35012)。

摘　　要：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.

关 键 词：human behavior research image semantic segmentation hop-connected full convolution network conditional random field network deep learning 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]