Gait recognition using FMCW radar and temporal convolutional deep neural netowrks

Addabbo, Pia and Bernardi, Mario Luca and Biondi, Filippo and Cimitile, Marta and Clemente, Carmine and Orlando, Danilo (2020) Gait recognition using FMCW radar and temporal convolutional deep neural netowrks. In: IEEE International Workshop on Metrology for Aerospace 2020, 2020-06-22 - 2020-06-24.

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    Abstract

    The capability of human identification in specific scenarios and in a quickly and accurately manner, is a critical aspect in various surveillance applications. In particular, in this context, classical surveillance systems are based on video cameras, requiring high computational/storing resources, which are very sensitive to light and weather conditions. In this paper, an efficient classifier based on deep learning is used for the purpose of identifying individuals features by resorting to the micro-Doppler data extracted from low-power frequency-modulated continuous-wave radar measurements. Results obtained through the application of a deep temporal convolutional neural networks confirms the applicability of deep learning to the problem at hand. Best obtained identification accuracy is 0.949 with an F-measure of 0.88 using a temporal window of four second.

    Creators(s): Addabbo, Pia, Bernardi, Mario Luca, Biondi, Filippo, Cimitile, Marta, Clemente, Carmine ORCID logoORCID: https://orcid.org/0000-0002-6665-693X and Orlando, Danilo;
    Item type:Conference or Workshop Item(Paper)
    ID code:73476
    Notes:© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Keywords:deep learning, gait recognition, low-power radar, micro doppler, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Strategic Research Themes > Measurement Science and Enabling Technologies
    Strategic Research Themes > Ocean, Air and Space
    Depositing user: Pure Administrator
    Date deposited:06 Aug 2020 10:27
    Last modified:15 Sep 2020 08:33
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/73476
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