Large-scale Vehicle Trajectory Reconstruction with Camera Sensing Network

Abstract

Vehicle trajectories provide essential information to understand the urban mobility and benefit a wide range of urban applications. State-of-the-art solutions for vehicle sensing may not build accurate and complete knowledge of all vehicle trajectories. In order to fill the gap, this paper proposes VeTrac, a comprehensive system that employs widely deployed traffic cameras as a sensing network to trace vehicle movements and reconstruct their trajectories in a large scale. VeTrac fuses mobility correlation and vision-based analysis to reduce uncertainties in identifying vehicles. A graph convolution process is employed to maintain the identity consistency across different camera observations, and a self-training process is invoked when aligning with the urban road network to reconstruct vehicle trajectories with confidence. Extensive experiments with real-world data input of over 7 million vehicle snapshots from over one thousand traffic cameras demonstrate that VeTrac achieves 98% accuracy for simple expressway scenario and 89% accuracy for complex urban environment. The achieved accuracy outperforms alternative solutions by 32% for expressway scenario and by 59% for complex urban environment.