Tracking and classifying objects with DAS data along railway

TL;DR

This paper demonstrates how distributed acoustic sensing (DAS) data from fiber-optic cables can be used to automatically detect, track, and classify vehicles and trains along a railway, providing valuable traffic monitoring insights.

Contribution

It introduces a novel method combining DAS data with a Kalman filter variant for automatic detection, tracking, and classification of objects along railway tracks.

Findings

Successful detection and tracking of cars and trains over a 50 km fiber-optic cable

Accurate counting of vehicles and trains over a day

Estimation of object velocities using the proposed method

Abstract

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.