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.

[thumbnail of Addabbo-etal-IEEE-IWMA-2020-Gait-recognition-using-FMCW-radar-and-temporal]
Preview
 Text. Filename: Addabbo_etal_IEEE_IWMA_2020_Gait_recognition_using_FMCW_radar_and_temporal.pdf
Accepted Author Manuscript
Download (761kB)| Preview

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.

ORCID iDs

Addabbo, Pia, Bernardi, Mario Luca, Biondi, Filippo, Cimitile, Marta, Clemente, Carmine ORCID logoORCID: https://orcid.org/0000-0002-6665-693X and Orlando, Danilo;
CORE (COnnecting REpositories)