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

Sensors

Carmine Clemente¹  MarioLuca Bernardi²  Filippo Biondi³  Danilo Orlando⁴  Pia Addabbo⁵  Marta Cimitile⁶ 

[1] Center for Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK;Department of Engineering, University of Sannio, Via Traiano, 1, 82100 Benevento, Italy;Electromagnetic Laboratory, Engineering Faculty, Università degli Studi dell’Aquila, Piazzale E. Pontieri, Monteluco di Roio, 67100 L’Aquila AQ , Italy;Engineering Faculty, Università degli Studi “Niccolò Cusano”, Via Don Carlo Gnocchi, 3, 00166 Rome, Italy;Science and Technology for Transportations Faculty, Università degli Studi “Giustino Fortunato”, Viale Raffale Delcogliano, 12, 82100 Benevento, Italy;Unitelma Sapienza, Viale Regina Elena, 295, 00161 Rome, Italy;

关键词: deep learning;    gait recognition;    low-power radar;    micro-Doppler;    human ID;   

DOI  :  10.3390/s21020381

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown