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

AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics