Gait recognition using FMCW radar and temporal convolutional deep neural netowrks

Pia Addabbo, Mario Luca Bernardi, Filippo Biondi, Marta Cimitile, Carmine Clemente, Danilo Orlando

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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.
Original languageEnglish
Number of pages5
Publication statusPublished - 5 Jul 2020
EventIEEE International Workshop on Metrology for Aerospace 2020 -
Duration: 22 Jun 202024 Jun 2020

Conference

ConferenceIEEE International Workshop on Metrology for Aerospace 2020
Period22/06/2024/06/20

Keywords

  • deep learning
  • gait recognition
  • low-power radar
  • micro doppler

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    Addabbo, P., Bernardi, M. L., Biondi, F., Cimitile, M., Clemente, C., & Orlando, D. (2020). Gait recognition using FMCW radar and temporal convolutional deep neural netowrks. Paper presented at IEEE International Workshop on Metrology for Aerospace 2020, .