Distributed acoustic sensing through fiber-optical cables can contribute to traffic monitoring systems. Using data from a day of field testing on a 50 km long fiber-optic cable along a railroad track in Norway, we detect and track cars and trains along a segment of the fiber-optic cable where the road runs parallel to the railroad tracks. We develop a method for automatic detection of events and then use these in a Kalman filter variant known as joint probabilistic data association for object tracking and classification. Model parameters are specified using in-situ log data along with the fiber-optic signals. Running the algorithm over an entire day, we highlight results of counting cars and trains over time and their estimated velocities.
Cite
@article{arxiv.2405.01140,
title = {Tracking and classifying objects with DAS data along railway},
author = {Simon L. B. Fredriksen and The Tien Mai and Kevin Growe and Jo Eidsvik},
journal= {arXiv preprint arXiv:2405.01140},
year = {2024}
}