3D Multi-Object Tracking: A Baseline and New Evaluation Metrics [AB3DMOT+PointRCNN]

Submitted on 26 Feb. 2022 23:01 by
Xinshuo Weng (Carnegie Mellon University)

Running time:0.0047s
Environment:1 core @ 2.5 Ghz (python)

Method Description:
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
Latex Bibtex:
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}

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.

CAR 83.92 % 85.30 % 83.95 % 88.21 %
PEDESTRIAN 38.39 % 64.88 % 39.33 % 91.22 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 88.17 % 97.19 % 92.46 % 33864 978 4542 8.79 % 37736 864
PEDESTRIAN 49.49 % 83.65 % 62.19 % 11550 2257 11788 20.29 % 14523 327

CAR 66.77 % 24.15 % 9.08 % 10 199
PEDESTRIAN 23.02 % 32.99 % 43.99 % 218 940

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.

eXTReMe Tracker