Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles
It addresses a critical problem for autonomous vehicles by enhancing multi-camera tracking consistency, though it appears incremental as it builds on existing 3D object detection methods.
This work tackles the challenge of achieving consistent multi-camera 3D object tracking for autonomous vehicles by proposing a Global Association Graph Model with Link Prediction, which improves detection accuracy and achieves state-of-the-art performance on the nuScenes dataset.
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.