A two-stage data association approach for 3D Multi-object Tracking
arXiv:2101.08684
Abstract
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.
References in corpus (6)
- MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
- Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
- AFDet: Anchor Free One Stage 3D Object Detection
- Probabilistic 3D Multi-Object Tracking for Autonomous Driving
- AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics
- 1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation