黑龙江科技大学学报2025，Vol.35Issue(2)：301-306,6.DOI:10.3969/j.issn.2095-7262.2025.02.020
改进GaitSet模型的煤矿井下人员步态识别方法
Gait recognition algorithm of underground coal mine personnel based on improved GaitSet model
摘要
Abstract
This paper is aimed at addressing the low accuracy and insufficient feature extraction in gait recognition models,and proposes a gait recognition method of underground coal mine personnel based on an improved GaitSet model.The study consits of introducing a multi-scale convolutional neural net-work for the feature extraction on the basis of the GaitSet model,adopting a multi-level pooling module for the retention of the the main gait features,enhancing the generalization ability of the model,and verifying the CASIA-B dataset and the self-built underground coal mine personnel gait dataset.The results show that after excluding the same perspective,the average recognition accuracy under the three states increa-ses by 0.53%,2.06%,and 1.35%respectively.And in the self-built underground coal mine personnel dataset,the average recognition accuracy increases by 3.63%.关键词
煤矿/步态识别/GaitSet/多尺度卷积/多级池化Key words
coal mine/gait recognition/GaitSet/multiscale convolution/multistage pooling分类
矿业与冶金引用本文复制引用
汝洪芳,赵晖,王国新..改进GaitSet模型的煤矿井下人员步态识别方法[J].黑龙江科技大学学报,2025,35(2):301-306,6.基金项目
黑龙江省重点研发计划指导类项目(GZ20220122) （GZ20220122）
黑龙江省省属高等学校基本科研业务费项目(2021-KYYWF-1480) （2021-KYYWF-1480）
