Search

Distributed Acoustic Sensing (DAS) is promising for traffic monitoring, but its extensive data and sensitivity to vibrations, causing noise, pose computational challenges. To address this, we propose a two-step deep-learning workflow with high efficiency and noise immunity for DAS-based traffic monitoring, focusing on instance vehicle trajectory segmentation and velocity estimation. Our approach begins by generating a diverse synthetic DAS dataset with labeled vehicle signals, tackling the issue of missing training labels in this field. This dataset is used to train a Convolutional Neural Network (CNN) to detect linear vehicle trajectories from the noisy DAS data in the time-space domain. However, due to significant noise, these trajectories are often fragmented and incomplete. To enhance accuracy, we introduce a second step involving the Hough transform. This converts detected linear features into point-like energy clusters in the Hough domain. Another CNN is then employed to focus on these energies, identifying the most significant points. The inverse Hough transform is applied to these points to reconstruct complete, distinct, and noise-free linear vehicle trajectories in the time-space domain. The Hough transform plays a crucial role by enforcing a local linearity constraint on the trajectories, enhancing continuity and noise immunity, and facilitating the separation of individual trajectories and estimation of vehicle velocities (indicated by trajectory slopes in the Hough domain). Our method has shown effectiveness in real-world datasets, proving its value in real-time processing of DAS data and applicability in similar traffic monitoring scenarios. All related codes and data are available at https://github.com/TTMuTian/itm/.