Roadside Cross-Camera Vehicle Tracking Combining Visual and Spatial-Temporal Information for a Cloud Control System

Abstract

Roadside cameras play a crucial role in road traffic, serving as an indispensable part of integrated vehicle-road-cloud systems due to their extensive visibility and monitoring capabilities. Nevertheless, these cameras face challenges in continuously tracking targets across perception domains. To address the issue of tracking vehicles across nonoverlapping perception domains between cameras, we propose a cross-camera vehicle tracking method within a Vehicle-Road-Cloud system that integrates visual and spatiotemporal information. A Gaussian model with microlevel traffic features is trained using vehicle information obtained through online tracking. Finally, the association of vehicle targets is achieved through the Gaussian model combining time and visual feature information. The experimental results indicate that the proposed system demonstrates excellent performance.