Training a Distributed Acoustic Sensing Traffic Monitoring Network With Video Inputs

Autor:	Cohen, Khen, Hen, Liav, Lellouch, Ariel
Rok vydání:	2024
Předmět:	Physics - Geophysics Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing Physics - Optics
Druh dokumentu:	Working Paper
Popis:	Distributed Acoustic Sensing (DAS) has emerged as a promising tool for real-time traffic monitoring in densely populated areas. In this paper, we present a novel concept that integrates DAS data with co-located visual information. We use YOLO-derived vehicle location and classification from camera inputs as labeled data to train a detection and classification neural network utilizing DAS data only. Our model achieves a performance exceeding 94% for detection and classification, and about 1.2% false alarm rate. We illustrate the model's application in monitoring traffic over a week, yielding statistical insights that could benefit future smart city developments. Our approach highlights the potential of combining fiber-optic sensors with visual information, focusing on practicality and scalability, protecting privacy, and minimizing infrastructure costs. To encourage future research, we share our dataset. Comment: 12 pages, 11 figures, 5 appendices. Shared dataset in: https://zenodo.org/records/14502092
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.12743 Zobrazit plný text záznamu View this record from Arxiv