A two-stage data association approach for 3D Multi-object Tracking
This work addresses a specific bottleneck in autonomous driving pipelines by improving tracking accuracy, though it is incremental as it adapts an existing method to a new domain.
The paper tackles the problem of data association in 3D multi-object tracking for autonomous driving by adapting a two-stage method from image-based tracking to the 3D setting, resulting in a performance of 0.587 AMOTA on the NuScenes validation set, which outperforms a baseline one-stage bipartite matching approach.
Multi-object tracking (MOT) is an integral part of any autonomous driving pipelines because itproduces trajectories which has been taken by other moving objects in the scene and helps predicttheir future motion. Thanks to the recent advances in 3D object detection enabled by deep learning,track-by-detection has become the dominant paradigm in 3D MOT. In this paradigm, a MOT systemis essentially made of an object detector and a data association algorithm which establishes track-to-detection correspondence. While 3D object detection has been actively researched, associationalgorithms for 3D MOT seem to settle at a bipartie matching formulated as a linear assignmentproblem (LAP) and solved by the Hungarian algorithm. In this paper, we adapt a two-stage dataassociation method which was successful in image-based tracking to the 3D setting, thus providingan alternative for data association for 3D MOT. Our method outperforms the baseline using one-stagebipartie matching for data association by achieving 0.587 AMOTA in NuScenes validation set.