工矿自动化2025，Vol.51Issue(9)：60-65,6.DOI:10.13272/j.issn.1671-251x.2025030104
基于DDPM-MBN的井下人员步态识别方法
Gait recognition method for underground personnel based on DDPM-MBN
摘要
Abstract
Existing millimeter-wave radar-based gait recognition methods are typically trained on small-scale datasets,resulting in poor model generalization and limited ability to extract effective global and local features from complex underground environments,which leads to low recognition accuracy.To address these issues,a gait recognition method for underground personnel based on the Denoising Diffusion Probabilistic Model(DDPM)and Multi-Branch Network(MBN)was proposed.The DDPM was used to denoise and augment the time-frequency spectrograms converted from radar echoes,effectively expanding the quantity of underground gait data and improving data quality.The MBN,consisting of one global branch and two local branches,extracted global gait features and local features of different granularities,enabling sufficient multi-scale feature extraction and improving the recognition of walking direction and speed.The Softmax loss and triplet loss were jointly employed to optimize coarse-grained features(2 048-dimensional features before dimensionality reduction)and fine-grained features(256-dimensional features after dimensionality reduction)in a collaborative manner,thereby enhancing the model's overall classification ability and feature discriminability.Experimental results showed that,on the self-built gait dataset,the DDPM-MBN model achieved Rank-1 accuracy and mean Average Precision(mAP)improvements of 8.05％and 16.96％,respectively,compared with ResNet-50.Compared with mainstream gait recognition models,the DDPM-MBN model achieved the best performance,with Rank-1 accuracy and mAP reaching 97.91％and 95.48％,respectively.关键词
人员步态识别/毫米波雷达/去噪扩散概率模型/多分支网络/时频谱图Key words
gait recognition/millimeter-wave radar/denoising diffusion probabilistic model/multi-branch network/time-frequency spectrogram分类
矿业与冶金引用本文复制引用
马进昇,宋一轩,刘家彤,潘红光,郭强,兰北亚,郭秀才..基于DDPM-MBN的井下人员步态识别方法[J].工矿自动化,2025,51(9):60-65,6.基金项目
陕西省秦创原"科学家+工程师"队伍建设项目(2022KXJ-38) （2022KXJ-38）
陕西省教育厅服务地方专项计划项目(23JC049). （23JC049）
