Method

3D Multi-Level Associations [3DMLA]


Submitted on 15 Jun. 2023 09:33 by
Minho Cho (Yonsei Univ.)

Running time:0.02 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@article{cho20233d,
title={3D LiDAR Multi-Object Tracking with
Short-Term and Long-Term Multi-Level
Associations},
author={Cho, Minho and Kim, Euntai},
journal={Remote Sensing},
volume={15},
number={23},
pages={5486},
year={2023},
publisher={MDPI}
}

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 75.65 % 71.92 % 80.02 % 77.31 % 83.33 % 83.73 % 88.39 % 86.62 %

Benchmark TP FP FN
CAR 30595 3797 1312

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.03 % 84.93 % 85.14 % 39 71.63 %

Benchmark MT rate PT rate ML rate FRAG
CAR 70.77 % 23.39 % 5.85 % 367

Benchmark # Dets # Tracks
CAR 31907 757

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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