The KITTI Vision Benchmark Suite

3D multi-object tracking (MOT) is an essential
component for many applications such as
autonomous driving and assistive robotics. Recent
work on 3D MOT focuses on developing accurate
systems giving less attention to practical
considerations such as computational cost and
system complexity. In contrast, this work
proposes a simple real-time 3D MOT system. 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 the 2D space and
standardized 3D MOT evaluation tools are missing
for a fair comparison of 3D MOT methods.
Therefore, we propose a new 3D MOT evaluation
tool along with three new metrics to
comprehensively evaluate 3D MOT methods. We show
that, although our system employs a combination
of classical MOT modules, we achieve state-of-
the-art 3D MOT performance on two 3D MOT

python=3.6

@article{Weng2020_AB3DMOT,
author = {Weng, Xinshuo and Wang, Jianren and Held,
David and Kitani, Kris},
journal = {IROS},
title = {{3D Multi-Object Tracking: A Baseline and
New Evaluation Metrics}},
year = {2020}
}