GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles
Journal article, 2023

Multi-object tracking (MOT) is among crucial applications in modern advanced driver assistance systems (ADAS) and autonomous driving (AD) systems. The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry. The development of random finite set (RFS) theory facilitates a mathematically rigorous treatment of the MOT problem, and different variants of RFS-based Bayesian filters have then been proposed. However, their effectiveness in the real ADAS and AD application is still an open problem. In this paper, it is demonstrated that the latest RFS-based Bayesian tracking framework could be superior to typical random vector-based Bayesian tracking framework via a systematic comparative study of both traditional random vector-based Bayesian filters with rule-based heuristic track maintenance and RFS-based Bayesian filters on the nuScenes validation dataset. An RFS-based tracker, namely Poisson multi-Bernoulli filter using the global nearest neighbor (GNN-PMB), is proposed to LiDAR-based MOT tasks. This GNN-PMB tracker is simple to use, and it achieves competitive results on the nuScenes dataset. Specifically, the proposed GNN-PMB tracker outperforms most state-of-the-art LiDAR-only trackers and LiDAR and camera fusion-based trackers, ranking the $3^{rd}$ among all LiDAR-only trackers on nuScenes 3D tracking challenge leader board1

at the time of submission. Our code is available at

Laser radar

Multi-object tracking

Radar tracking

Bayes methods



random finite set-based Bayesian filters

Three-dimensional displays

random vector-based Bayesian filters

autonomous driving



Feature extraction


Jianan Liu

Vitalent Consulting

Liping Bai

Beihang University

Yuxuan Xia

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Tao Huang

James Cook University

Bing Zhu

Beihang University

IEEE Transactions on Intelligent Vehicles

23798858 (eISSN)

Vol. 8 2 1176-1189

Subject Categories

Software Engineering

Embedded Systems

Computer Vision and Robotics (Autonomous Systems)



More information

Latest update

7/7/2023 6