  The development of autonomous vehicles generates a tremendous demand for a
low-cost solution with a complete set of camera sensors capturing the
environment around the car. It is essential for object detection and tracking
to address these new challenges in multi-camera settings. In order to address
these challenges, this work introduces novel Single-Stage Global Association
Tracking approaches to associate one or more detection from multi-cameras with
tracked objects. These approaches aim to solve fragment-tracking issues caused
by inconsistent 3D object detection. Moreover, our models also improve the
detection accuracy of the standard vision-based 3D object detectors in the
nuScenes detection challenge. The experimental results on the nuScenes dataset
demonstrate the benefits of the proposed method by outperforming prior
vision-based tracking methods in multi-camera settings.
