论文标题
自动驾驶汽车移动中的多相机多3D对象跟踪
Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles
论文作者
论文摘要
自动驾驶汽车的开发提供了一个机会，可以使一组完整的相机传感器捕获汽车周围的环境。因此，对于应对新挑战的对象检测和跟踪非常重要，例如在相机的视图中取得一致的结果。为了应对这些挑战，这项工作提出了一种新的全球关联图模型，采用链路预测方法来预测现有的曲目位置，并通过跨科运动建模和外观重新识别与踪迹联系。这种方法旨在解决由3D对象检测不一致引起的问题。此外，我们的模型利用了在Nuscenes检测挑战中提高标准3D对象检测器的检测准确性。 Nuscenes数据集上的实验结果证明了在现有基于视觉的跟踪数据集中产生SOTA性能的提议方法的好处。
The development of autonomous vehicles provides an opportunity to have a complete set of camera sensors capturing the environment around the car. Thus, it is important for object detection and tracking to address new challenges, such as achieving consistent results across views of cameras. To address these challenges, this work presents a new Global Association Graph Model with Link Prediction approach to predict existing tracklets location and link detections with tracklets via cross-attention motion modeling and appearance re-identification. This approach aims at solving issues caused by inconsistent 3D object detection. Moreover, our model exploits to improve the detection accuracy of a standard 3D object detector in the nuScenes detection challenge. The experimental results on the nuScenes dataset demonstrate the benefits of the proposed method to produce SOTA performance on the existing vision-based tracking dataset.
