Volume 13 Issue 1
Feb.  2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2024  >  13(1): 68-86
YANG Xiaopeng, GAO Weicheng, and QU Xiaodong. Human anomalous gait termination recognition via through-the-wall radar based on micro-Doppler corner features and Non-Local mechanism[J]. Journal of Radars, 2024, 13(1): 68–86. doi: 10.12000/JR23181
Citation: YANG Xiaopeng, GAO Weicheng, and QU Xiaodong. Human anomalous gait termination recognition via through-the-wall radar based on micro-Doppler corner features and Non-Local mechanism[J]. Journal of Radars, 2024, 13(1): 68–86. doi: 10.12000/JR23181
shu

Human Anomalous Gait Termination Recognition via Through-the-wall Radar Based on Micro-Doppler Corner Features and Non-Local Mechanism

DOI: 10.12000/JR23181

  • School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds:  The National Natural Science Foundation of China (61860206012), Beijing Institute of Technology Research Fund Program for Young Scholars
More Information
  • Corresponding author: QU Xiaodong, xdqu@bit.edu.cn
  • Received Date: 2023-10-04
  • Rev Recd Date: 2023-11-22
    • Available Online: 2023-12-05
  • Publish Date: 2023-12-15
    Abstract
  • Through-the-wall radar can penetrate walls and realize indoor human target detection. Deep learning is commonly used to extract the micro-Doppler signature of a target, which can be used to effectively identify human activities behind obstacles. However, the test accuracy of the deep-learning-based recognition methods is low with poor generalization ability when different testers are invited to generate the training set and test set. Therefore, this study proposes a method for recognition of anomalous human gait termination based on micro-Doppler corner features and Non-Local mechanism. In this method, Harris and Moravec detectors are utilized to extract the corner features of the radar image, and the corner feature dataset is established in this manner. Thereafter, multilink parallel convolutions and the Non-Local mechanism are utilized to construct the global contextual information extraction network to learn the global distribution characteristics of the image pixels. The semantic feature maps are generated by repeating four times the global contextual information extraction network. Finally, the probabilities of human activities are predicted using a multilayer perceptron. The numerical simulation and experimental results demonstrate that the proposed method can effectively identify such abnormal gait termination activities as sitting, lying down, and falling, among others, which occur in the process of indoor human walking, and successfully control the generalization accuracy error to be no more than $ 6.4\% $ under the premise of increasing the recognition accuracy and robustness.

     

    • FullText(HTML)
  • loading
    • References(43)
  • [1]
    刘天亮, 谯庆伟, 万俊伟, 等. 融合空间-时间双网络流和视觉注意的人体行为识别[J]. 电子与信息学报, 2018, 40(10): 2395–2401. doi: 10.11999/JEIT171116

    LIU Tianliang, QIAO Qingwei, WAN Junwei, et al. Human action recognition via spatio-temporal dual network flow and visual attention fusion[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2395–2401. doi: 10.11999/JEIT171116
    [2]
    吴培良, 杨霄, 毛秉毅, 等. 一种视角无关的时空关联深度视频行为识别方法[J]. 电子与信息学报, 2019, 41(4): 904–910. doi: 10.11999/JEIT180477

    WU Peiliang, YANG Xiao, MAO Bingyi, et al. A perspective-independent method for behavior recognition in depth video via temporal-spatial correlating[J]. Journal of Electronics & Information Technology, 2019, 41(4): 904–910. doi: 10.11999/JEIT180477
    [3]
    金添, 何元, 李新羽, 等. 超宽带雷达人体行为感知研究进展[J]. 电子与信息学报, 2022, 44(4): 1147–1155. doi: 10.11999/JEIT211044

    JIN Tian, HE Yuan, LI Xinyu, et al. Advances in human activity sensing using ultra-wide band radar[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1147–1155. doi: 10.11999/JEIT211044
    [4]
    吕温, 徐贵力, 程月华, 等. 基于局部时空特征的人体行为软分类识别[J]. 计算机与现代化, 2014(3): 94–98, 103. doi: 10.3969/j.issn.1006-2475.2014.03.023

    LV Wen, XU Guili, CHENG Yuehua, et al. Soft Classification in action recognition based on local spatio-temporal features[J]. Computer and Modernization, 2014(3): 94–98, 103. doi: 10.3969/j.issn.1006-2475.2014.03.023
    [5]
    丁一鹏, 厍彦龙. 穿墙雷达人体动作识别技术的研究现状与展望[J]. 电子与信息学报, 2022, 44(4): 1156–1175. doi: 10.11999/JEIT211051

    DING Yipeng and SHE Yanlong. Research status and prospect of human movement recognition technique using through-wall radar[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1156–1175. doi: 10.11999/JEIT211051
    [6]
    金添, 宋勇平, 崔国龙, 等. 低频电磁波建筑物内部结构透视技术研究进展[J]. 雷达学报, 2021, 10(3): 342–359. doi: 10.12000/JR20119

    JIN Tian, SONG Yongping, CUI Guolong, et al. Advances on penetrating imaging of building layout technique using low frequency radio waves[J]. Journal of Radars, 2021, 10(3): 342–359. doi: 10.12000/JR20119
    [7]
    金添, 宋勇平. 超宽带雷达建筑物结构稀疏成像[J]. 雷达学报, 2018, 7(3): 275–284. doi: 10.12000/JR18031

    JIN Tian and SONG Yongping. Sparse imaging of building layouts in ultra-wideband radar[J]. Journal of Radars, 2018, 7(3): 275–284. doi: 10.12000/JR18031
    [8]
    夏正欢, 张群英, 叶盛波, 等. 一种便携式伪随机编码超宽带人体感知雷达设计[J]. 雷达学报, 2015, 4(5): 527–537. doi: 10.12000/JR15027

    XIA Zhenghuan, ZHANG Qunying, YE Shengbo, et al. Design of a handheld pseudo random coded UWB radar for human sensing[J]. Journal of Radars, 2015, 4(5): 527–537. doi: 10.12000/JR15027
    [9]
    FALCONER D G, FICKLIN R W, and KONOLIGE K G. Robot-mounted through-wall radar for detecting, locating, and identifying building occupants[C]. Millennium Conference. IEEE International Conference on Robotics and Automation, San Francisco, USA, 2000: 1868–1875.
    [10]
    FALCONER D G, FICKLIN R W, and KONOLIGE K G. Detection, location, and identification of building occupants using a robot-mounted through-wall radar[C]. SPIE 4037, Digitization of the Battlespace V and Battlefield Biomedical Technologies II, Orlando, USA, 2000: 72–81.
    [11]
    LAI C P, RUAN Qing, and NARAYANAN R M. Hilbert-Huang Transform (HHT) processing of through-wall noise radar data for human activity characterization[C]. 2007 IEEE Workshop on Signal Processing Applications for Public Security and Forensics, Washington, USA, 2007: 1–6.
    [12]
    LI Jing, ZENG Zhaofa, SUN Jiguang, et al. Through-wall detection of human Being’s movement by UWB radar[J]. IEEE Geoscience & Remote Sensing Letters, 2012, 9(6): 1079–1083. doi: 10.1109/LGRS.2012.2190707
    [13]
    QI Fugui, LIANG Fulai, LV Hao, et al. Detection and classification of finer-grained human activities based on stepped-frequency continuous-wave through-wall radar[J]. Sensors, 2016, 16(6): 885. doi: 10.3390/s16060885
    [14]
    QI Fugui, LV Hao, LIANG Fulai, et al. MHHT-based method for analysis of micro-doppler signatures for human finer-grained activity using through-wall SFCW Radar[J]. Remote Sensing, 2017, 9(3): 260. doi: 10.3390/rs9030260
    [15]
    PENG Yansong and GUO Shisheng. Detailed feature representation and analysis of low frequency UWB Radar range profile for improving through-wall human activity recognition[C]. 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, 2020: 1–6.
    [16]
    QI Fugui, LI Zhao, MA Yangyang, et al. Generalization of channel micro-Doppler capacity evaluation for improved finer-grained human activity classification using MIMO UWB radar[J]. IEEE Transactions on Microwave Theory and Techniques, 2021, 69(11): 4748–4761. doi: 10.1109/TMTT.2021.3076055
    [17]
    WANG Xiang, CHEN Pengyun, XIE Hangchen, et al. Through-wall human activity classification using complex-valued convolutional neural network[C]. 2021 IEEE Radar Conference (RadarConf21), Atlanta, USA, 2021: 1–4.
    [18]
    ARBABI E, BOULIC R, and THALMANN D. A fast method for finding range of motion in the Human joints[C]. The 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 2007: 5079–5082.
    [19]
    MAAREF N, MILLOT P, PICHOT C, et al. A study of UWB FM-CW radar for the detection of human beings in motion inside a building[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(5): 1297–1300. doi: 10.1109/TGRS.2008.2010709
    [20]
    JIN Tian and YAROVOY A. A through-the-wall radar imaging method based on a realistic model[J]. International Journal of Antennas and Propagation, 2015, 2015: 539510. doi: 10.1155/2015/539510
    [21]
    MA Ruowen, LIU Weiguo, MIAO Chen, et al. The simulation of human walking micro-Doppler echo and comparison of time-frequency analysis method[C]. The First International Conference on Electronics Instrumentation & Information Systems (EIIS), Harbin, China, 2017: 1–6.
    [22]
    JOHANSSON T, RAHM J, GUSTAVSSON J, et al. Through-the-wall detection of human activity[C]. SPIE 8021, Radar Sensor Technology XV, Orlando, USA, 2011: 446–454.
    [23]
    FAIRCHILD D P and NARAYANAN R M. Classification of human motions using empirical mode decomposition of human micro-Doppler signatures[J]. IET Radar, Sonar & Navigation, 2014, 8(5): 425–434. doi: 10.1049/iet-rsn.2013.0165
    [24]
    OH B S, GUO Xin, and LIN Zhiping. A UAV classification system based on FMCW radar micro-Doppler signature analysis[J]. Expert Systems with Applications, 2019, 132: 239–255. doi: 10.1016/j.eswa.2019.05.007
    [25]
    SAFY M, SHI Guangming, and AMEIN A S. Semi-automatic image registration using Harris corner detection and RANdom SAmple Consensus (RANSAC)[C]. IET International Radar Conference 2013, Xi’an, China, 2013: 1–4.
    [26]
    KUMARI M S and SHEKAR B H. On the use of Moravec operator for text detection in document images and video frames[C]. 2011 International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, India: 2011: 910–914.
    [27]
    CAO Yue, XU Jiarui, LIN S, et al. GCNet: Non-local networks meet squeeze-excitation networks and beyond[C]. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2020: 1971–1980.
    [28]
    VISHWAKARMA S, LI Wenda, TANG Chong, et al. SimHumalator: An open-source end-to-end radar simulator for human activity recognition[J]. IEEE Aerospace and Electronic Systems Magazine, 2022, 37(3): 6–22. doi: 10.1109/MAES.2021.3138948
    [29]
    BHAT S A, DAR M A, SZCZUKO P, et al. Sensing direction of human motion using Single-Input-Single-Output (SISO) channel model and neural networks[J]. IEEE Access, 2022, 10: 56823–56844. doi: 10.1109/ACCESS.2022.3177273
    [30]
    CHENG Can, LING Fei, GUO Shisheng, et al. A real-time human activity recognition method for through-the-wall radar[C]. 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, 2020: 1–5.
    [31]
    AN Qiang, WANG Shuoguang, YAO Lei, et al. RPCA-based high resolution through-the-wall human motion feature extraction and classification[J]. IEEE Sensors Journal, 2021, 21(17): 19058–19068. doi: 10.1109/JSEN.2021.3088122
    [32]
    CHEN Pengyun, GUO Shisheng, LI Huquan, et al. Through-wall human motion recognition based on transfer learning and ensemble learning[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 3505305. doi: 10.1109/LGRS.2021.3070374
    [33]
    WANG Xiang, WANG Yumiao, GUO Shisheng, et al. Capsule network with multiscale feature fusion for hidden human activity classification[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 2504712. doi: 10.1109/TIM.2023.3238749
    [34]
    SAVAGE R, PALAFOX L F, MORRISON C T, et al. A Bayesian approach to subkilometer crater shape analysis using individual HiRISE images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 5802–5812. doi: 10.1109/TGRS.2018.2825608
    [35]
    WINKELMANN S, SCHAEFFTER T, EGGERS H, et al. SNR enhancement in radial SSFP imaging using partial k-space averaging[J]. IEEE Transactions on Medical Imaging, 2005, 24(2): 254–262. doi: 10.1109/TMI.2004.840845
    [36]
    KRIG S. Computer Vision Metrics: Survey, Taxonomy, and Analysis[M]. Berkeley: Apress, 2014: 85.
    [37]
    VISHWAKARMA S, LI Wenda, ADVE R, et al. Learning salient features in radar micro-Doppler signatures using Attention Enhanced Alexnet[C]. International Conference on Radar Systems (RADAR 2022), Hybrid Conference, UK, 2022: 190–195.
    [38]
    LI Jiefang, CHEN Xiaolong, YU Gang, et al. High-precision human activity classification via radar micro-Doppler signatures based on deep neural network[C]. IET International Radar Conference (IET IRC 2020), 2020: 1124–1129.
    [39]
    JIA Yong, GUO Yong, SONG Ruiyuan, et al. ResNet-based counting algorithm for moving targets in through-the-wall radar[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(6): 1034–1038. doi: 10.1109/LGRS.2020.2990742
    [40]
    CHUMA E L and IANO Y. Human movement recognition system using CW Doppler radar sensor with FFT and convolutional neural network[C]. 2020 IEEE MTT-S Latin America Microwave Conference (LAMC 2020), Cali, Colombia, 2021: 1–4.
    [41]
    ZHAO Wenwei, WANG Runze, QI Yunliang, et al. BASCNet: Bilateral adaptive spatial and channel attention network for breast density classification in the mammogram[J]. Biomedical Signal Processing and Control, 2021, 70: 103073. doi: 10.1016/j.bspc.2021.103073
    [42]
    RAJAMANI K T, SIEBERT H, and HEINRICH M P. Dynamic deformable attention network (DDANet) for COVID-19 lesions semantic segmentation[J]. Journal of Biomedical Informatics, 2021, 119: 103816. doi: 10.1016/j.jbi.2021.103816
    [43]
    CHI Lu, YUAN Zehuan, MU Yadong, et al. Non-local neural networks with grouped bilinear attentional transforms[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2020: 11801–11810.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(1330) PDF downloads(278) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return