Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition

Autor: Pia Addabbo, Mario Luca Bernardi, Filippo Biondi, Marta Cimitile, Carmine Clemente, Danilo Orlando
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Sensors, Vol 21, Iss 2, p 381 (2021)
Druh dokumentu: article
ISSN: 1424-8220
93561547
DOI: 10.3390/s21020381
Popis: 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.
Databáze: Directory of Open Access Journals
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