论文标题
通过单阶段全球关联方法，多相机多对象跟踪移动
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach
论文作者
论文摘要
自动驾驶汽车的开发产生了对低成本解决方案的巨大需求，该解决方案具有完整的相机传感器，可捕获汽车周围的环境。对象检测和跟踪至关重要，以解决多相机设置中的这些新挑战。为了应对这些挑战，这项工作介绍了新颖的单阶段全球关联方法，以将多盘从多胶面包剂与跟踪对象相关联。这些方法旨在解决由不一致的3D对象检测引起的碎片跟踪问题。此外，我们的模型还提高了Nuscenes检测挑战中基于标准视觉的3D对象检测器的检测准确性。 Nuscenes数据集上的实验结果通过在多相机设置中优于基于视觉的跟踪方法来证明该方法的好处。
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.
