  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/.
