Expressway Traffic Trajectory Recognition on DAS Vibration Spatiotemporal Images

Abstract

Distributed Acoustic Sensing (DAS) can capture spatio-temporal vibration images of vehicles on expressways, which can be utilized for traffic monitoring. Compared to ubiquitously deployed cameras, DAS traffic monitoring offers advantages such as full coverage, resistance to environmental interference, low computational requirements, and cost-effectiveness. However, real-world complexities result in challenges for DAS traffic images, including low signal-to-noise ratio, signal missing, and uneven intensity. As DAS traffic applications are still in their early stages, effective solutions to these challenges are yet to be developed. This paper proposes a new deep learning method named DAS High Speed Traffic Trajectory (DAS-HTT) network, which contributes threefold: (i) Multi-Scale Context Extraction Module (MSCE) effectively enlarges the receptive field to capture long-range contextual information comprehensively; (ii) Stripe Convolution Decoder (SCD) acquires remote information along four directions, preventing irrelevant region interference in feature learning; (iii) Hierarchically Hough Transform Fusion Decoder (HHTFD) introduces the structural information of trajectory linearity, reducing the reliance on label data while enhancing trajectory continuity. We conducted experiments on an operating expressway, demonstrating that DAS-HTT outperforms existing methods across seven metrics, providing trajectories that are more consistent with ground truthes.