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arXiv · 2211.09663

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

Pha Nguyen·Kha Gia Quach·Chi Nhan Duong·Son Lam Phung·Ngan Le·Khoa Luu
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Abstract

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.

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Pha Nguyen, Kha Gia Quach, Chi Nhan Duong, Son Lam Phung, Ngan Le, Khoa Luu. 2022-11-17. Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach. https://arxiv.org/abs/2211.09663

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