Development of a multiple‐camera 3D vehicle tracking system for traffic data collection at intersections

Abstract

In this paper, we propose and develop a multiple-camera 3D vehicle tracking system for traffic data collection at intersections. Assuming a simple 3D cuboid model for the vehicle, the developed system allows 3D vehicle dimension estimation using fusion of information from multiple cameras. Using a common rectangular road pattern, each camera is first individually calibrated and then jointly post-optimised. Then, the developed 3D vehicle tracking system takes synchronised images from multiple cameras as inputs and processes 2D image frames using object segmentation techniques to derive vehicle silhouettes. After 2D vehicle segmentation, objects in the 2D image frames are projected to the 3D real world to allow estimation of vehicle length and width. The height of the object is sought in the image view that would create the top quadrilateral of the vehicle that has the edge furthest away from the vehicle base quadrilateral. With Kalman filter based vehicle tracking, interested traffic data, such as vehicle count, are derived from the vehicle trajectory. Real-world experimental results for an intersection with two cameras have shown that the developed 3D vehicle tracking system can reliably estimate 3D vehicle dimensions and improve accuracy of traffic data collection compared to a single-camera system.