  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.
