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

Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach