  3D multi-object tracking (MOT) is essential to applications such as
autonomous driving. Recent work focuses on developing accurate systems giving
less attention to computational cost and system complexity. In contrast, this
work proposes a simple real-time 3D MOT system with strong performance. Our
system first obtains 3D detections from a LiDAR point cloud. Then, a
straightforward combination of a 3D Kalman filter and the Hungarian algorithm
is used for state estimation and data association. Additionally, 3D MOT
datasets such as KITTI evaluate MOT methods in 2D space and standardized 3D MOT
evaluation tools are missing for a fair comparison of 3D MOT methods. We
propose a new 3D MOT evaluation tool along with three new metrics to
comprehensively evaluate 3D MOT methods. We show that, our proposed method
achieves strong 3D MOT performance on KITTI and runs at a rate of $207.4$ FPS
on the KITTI dataset, achieving the fastest speed among modern 3D MOT systems.
Our code is publicly available at http://www.xinshuoweng.com/projects/AB3DMOT.
