Temporal convolutional neural networks for radar micro-Doppler based gait recognition
Addabbo, Pia and Bernardi, Mario Luca and Biondi, Filippo and Cimitile, Marta and Clemente, Carmine and Orlando, Danilo (2021) Temporal convolutional neural networks for radar micro-Doppler based gait recognition. Sensors, 21 (2). 381. ISSN 1424-8220 (https://doi.org/10.3390/s21020381)
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Abstract
The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion and have high computational and storing requirements in order to perform human identification. Moreover, they are strongly influenced by external factors (e.g., light and weather). This paper proposes an approach based on a temporal convolutional deep neural networks classifier applied to radar micro-Doppler signatures in order to identify individuals. Both sensor and processing requirements ensure a low size weight and power profile, enabling large scale deployment of discrete human identification systems. The proposed approach is assessed on real data concerning 106 individuals. The results show good accuracy of the classifier (the best obtained accuracy is 0.89 with an F1-score of 0.885) and improved performance when compared to other standard approaches.
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Item type: Article ID code: 75052 Dates: DateEvent7 January 2021Published1 January 2021AcceptedSubjects: 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 SpaceDepositing user: Pure Administrator Date deposited: 13 Jan 2021 14:49 Last modified: 27 Aug 2024 21:47 URI: https://strathprints.strath.ac.uk/id/eprint/75052