Science and Technology for Transportations Faculty, Università degli Studi "Giustino Fortunato", Viale Raffale Delcogliano, 12, 82100 Benevento, Italy.
Department of Engineering, University of Sannio, Via Traiano, 1, 82100 Benevento, Italy.
Sensors (Basel). 2021 Jan 7;21(2):381. doi: 10.3390/s21020381.
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.
传感器在特定场景下识别个体的能力是公共安全等敏感领域的一个重要话题。传统的方法涉及到摄像机；然而，基于摄像机的监控系统缺乏灵活性，并且在进行人员识别时需要大量的计算和存储资源。此外，它们还受到外部因素（例如光线和天气）的强烈影响。本文提出了一种基于时间卷积深度神经网络分类器的方法，该方法应用于雷达微多普勒特征，以实现人员识别。传感器和处理要求都确保了低尺寸、重量和功率的特性，从而能够大规模部署离散的人员识别系统。所提出的方法在涉及 106 个人的真实数据上进行了评估。结果表明，分类器具有较高的准确性（最佳准确率为 0.89，F1 得分为 0.885），并且与其他标准方法相比性能有所提高。
