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.

本文提出了一个新的全局关联图模型与链接预测方法，以预测现有的 tracklets 位置并通过交叉注意力运动建模和外观重新识别将检测与 tracklets 链接起来，以解决由于不一致的 3D 对象检测引起的问题，并提高 nuScenes 检测挑战中标准 3D 对象检测器的检测准确率。实验结果表明，该方法在现有基于视觉的跟踪数据集上表现出了 SOTA 的性能。