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Evaluating mmWave Sensing Ability of Recognizing Multi-people Under Practical Scenarios

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Green, Pervasive, and Cloud Computing (GPC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12398))

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Abstract

Visual tracking is a scheme to locate people’s position in space. However, there are privacy concerns that raw video may cause leakage of personal information. Many people do not accept cameras deployed in their homes or workspaces. Recently, millimeter-wave (mmWave) gait recognition has been recognized as an alternative solution, which has the advantages of low power consumption and user privacy protection. However, performance analysis particularly under multi-person application scenarios in different environments remains to be explored. In this work, we collect and evaluate over the mmWave sensing point cloud dataset from mmWave FMCW radars. Our study reveals the change of point cloud as people population and their distance varies .

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Acknowledgements

The work is supported by National Key R&D Program of China (2019YFB2102202), NSFC (61772084, 61720106007, 61832010), the Funds for Creative Research Groups of China (61921003), the 111 Project (B18008), the Fundamental Research Funds for the Central Universities (2019XD-A13).

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Lipeng Feng, Shibo Du, Zhen Meng, Anfu Zhou & Huadong Ma

Authors
  1. Lipeng Feng
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  2. Shibo Du
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  3. Zhen Meng
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  4. Anfu Zhou
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  5. Huadong Ma
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Corresponding author

Correspondence to Anfu Zhou .

Editor information

Editors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, China

    Zhiwen Yu

  2. University of Mannheim, Mannheim, Germany

    Christian Becker

  3. Chinese University of Hong Kong, Shatin, Hong Kong

    Guoliang Xing

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Feng, L., Du, S., Meng, Z., Zhou, A., Ma, H. (2020). Evaluating mmWave Sensing Ability of Recognizing Multi-people Under Practical Scenarios. In: Yu, Z., Becker, C., Xing, G. (eds) Green, Pervasive, and Cloud Computing. GPC 2020. Lecture Notes in Computer Science(), vol 12398. Springer, Cham. https://doi.org/10.1007/978-3-030-64243-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-64243-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64242-6

  • Online ISBN: 978-3-030-64243-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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