Method

Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving [la] [on] [JMODT]
https://github.com/Kemo-Huang/JMODT

Submitted on 3 Mar. 2021 08:56 by
Kemiao Huang (Southern University of Science and Technology)

Running time:0.01 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Our MOT system performs online joint object
detection and tracking, robust affinity computation
and comprehensive data association.
Parameters:
See the code for details.
Latex Bibtex:
@inproceedings{huang2021joint,
title={Joint multi-object detection and tracking
with camera-LiDAR fusion for autonomous driving},
author={Huang, Kemiao and Hao, Qi},
booktitle={2021 IEEE/RSJ International Conference
on Intelligent Robots and Systems (IROS)},
pages={6983--6989},
year={2021},
organization={IEEE}
}

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 70.73 % 73.45 % 68.76 % 78.67 % 84.02 % 72.46 % 88.02 % 86.95 %

Benchmark TP FP FN
CAR 30954 3438 1249

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.35 % 85.37 % 86.37 % 350 72.19 %

Benchmark MT rate PT rate ML rate FRAG
CAR 77.39 % 19.69 % 2.92 % 693

Benchmark # Dets # Tracks
CAR 32203 1146

This table as LaTeX

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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