Abstract
This paper presents a Multi-Object Tracking (MOT) framework that fuses radar and camera data to enhance tracking efficiency while minimizing manual interventions. Contrary to many studies that underutilize radar and assign it a supplementary role--despite its capability to provide accurate range/depth information of targets in a world 3D coordinate system--our approach positions radar in a crucial role. Meanwhile, this paper utilizes common features to enable online calibration to autonomously associate detections from radar and camera. The main contributions of this work include: (1) the development of a radar-camera fusion MOT framework that exploits online radar-camera calibration to simplify the integration of detection results from these two sensors, (2) the utilization of common features between radar and camera data to accurately derive real-world positions of detected objects, and (3) the adoption of feature matching and category-consistency checking to surpass the limitations of mere position matching in enhancing sensor association accuracy. To the best of our knowledge, we are the first to investigate the integration of radar-camera common features and their use in online calibration for achieving MOT. The efficacy of our framework is demonstrated by its ability to streamline the radar-camera mapping process and improve tracking precision, as evidenced by real-world experiments conducted in both controlled environments and actual traffic scenarios. Code is available at this https URL
Abstract (translated)
这篇论文提出了一种多目标跟踪（MOT）框架，该框架融合了雷达和相机数据以提高跟踪效率，并减少人工干预。与许多将雷达的作用局限于辅助角色且未能充分利用其提供世界三维坐标系统中目标精确距离/深度信息的能力的研究不同，我们的方法使雷达发挥了关键作用。同时，本文利用共同特征来实现在线校准，以便自主关联来自雷达和相机的检测结果。 本工作的主要贡献包括： 1. 开发了一种基于雷达-相机融合的MOT框架，并通过利用在线雷达-相机校准简化了这两种传感器检测结果的整合。 2. 利用雷达与相机数据之间的共同特征来准确推导出被检测对象在真实世界中的位置。 3. 采用特征匹配和类别一致性检查，超越仅基于位置匹配的方法限制，在提高传感器关联准确性方面取得了进步。 据我们所知，这是我们首次研究雷达-相机共性特征的集成及其在线校准的应用，以实现MOT。我们的框架的有效性通过其能够简化雷达-相机映射过程并提升跟踪精度的能力得到了证明，这一点在控制环境和实际交通场景中的真实世界实验中都得到了验证。 代码可在[提供的URL]获取。
URL
https://arxiv.org/abs/2510.20794
