Training a Distributed Acoustic Sensing Traffic Monitoring Network With Video Inputs
This work addresses traffic monitoring for smart cities, offering a practical and scalable solution with privacy benefits, though it is incremental as it combines existing methods.
The paper tackles traffic monitoring by integrating Distributed Acoustic Sensing (DAS) data with video inputs to train a neural network, achieving over 94% performance for detection and classification with about a 1.2% false alarm rate.
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.