A fiber-optic traffic monitoring network trained with video inputs

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 new approach that integrates DAS data with co-located, calibrated video recordings. We use YOLO-derived vehicle location and classification from video inputs as labeled data to train a detection and classification neural network that uses DAS data only. The model is applied in areas with and without video coverage. It achieves about success in detection and classification, and about false alarm rate when compared to YOLO outputs. We illustrate the model’s application in monitoring a week of traffic, yielding statistical insights that could benefit future smart city developments. Our approach highlights the potential of combining fiber-optic sensors and cameras, focusing on practicality and scalability, protecting privacy, and minimizing infrastructure costs. To encourage future research, we share our datasets.