| 基于双流神经网络的煤矿井下人员步态识别方法 |
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| 引用本文： | 刘晓阳,刘金强,郑昊琳. 基于双流神经网络的煤矿井下人员步态识别方法[J]. 矿业科学学报, 2021, 6(2): 218-227. DOI: 10.19606/j.cnki.jmst.2021.02.010 |
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| 作者姓名： | 刘晓阳 刘金强 郑昊琳 |
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| 作者单位： | 中国矿业大学(北京)机电与信息工程学院,北京 100083;中国矿业大学(北京)机电与信息工程学院,北京 100083;中国矿业大学(北京)机电与信息工程学院,北京 100083 |
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| 基金项目： | 国家重点研发计划2016YFC0801800国家自然科学基金51674269中央高校基本科研业务费专项资金2020YJSJD11 |
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| 摘 要： | 人脸、指纹和虹膜等生物识别方法在井下复杂环境限制下常常比较模糊,导致基于这些生物特征的煤矿井下人员身份识别率不高.本文在残差神经网络和栈式卷积自动编码器的基础上,提出了一种基于双流神经网络(TS-GAIT)的步态识别方法.主要利用残差神经网络提取步态模式中包含时空信息的动态特征,利用栈式卷积自动编码器提取包含生理信息的...
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| 关 键 词： | 煤矿井下人员 步态识别 栈式卷积自动编码器 残差神经网络 双流神经网络 |
| 收稿时间： | 2020-05-21 |
Gait recognition method of coal mine personnel based on Two-Stream neural network |
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| Affiliation: | School of Mechanical Electronic and Information Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China |
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| Abstract: | Biometric methods such as human faces, fingerprints, and irises are relatively mature, but the images of these biometric methods often become blurred under the limitations of the complex underground environment, which leads to the problem of low identification rate of underground coal mine personnel.To solve this problem, a Two-Stream neural network(TS-GAIT)gait recognition method is proposed based on the residual neural network and the stacked convolutional autoencoder in this paper.The residual neural network is mainly used to extract the dynamic deep features containing spatiotemporal information in the gait pattern.The stacked convolutional autoencoder is used to extract the static invariant features containing physiological information.Moreover, a novel feature fusion method is adopted to achieve the fusion and representation of dynamic and static invariant features.The extracted features are robust to angle, clothing and carrying conditions.The method is evaluated on the challenging CASIA-B gait dataset and the collected gait dataset of coal miners(CM-GAIT).The experimental results show that the method is effective and feasible for gait recognition of underground coal mine personnel.Compared with other gait recognition methods, the accuracy rate has been significantly increased. |
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