Method

EAFFMOT[on][la] [EAFFMOT]


Submitted on 1 Oct. 2023 21:16 by
Jingyi Jin (Jilin University)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We propose a real-time 3D multi-object tracking
framework based on tracking-by-detection paradigm.
Parameters:
no
Latex Bibtex:
@article{jin20243d,
title={3D multi-object tracking with boosting
data association and improved trajectory
management mechanism},
author={Jin, Jingyi and Zhang, Jindong and
Zhang, Kunpeng and Wang, Yiming and Ma, Yuanzhi
and Pan, Dongyu},
journal={Signal Processing},
volume={218},
pages={109367},
year={2024},
publisher={Elsevier}
}

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 72.28 % 71.97 % 73.08 % 77.05 % 83.77 % 76.39 % 88.66 % 86.73 %
PEDESTRIAN 40.20 % 35.59 % 45.63 % 38.35 % 60.03 % 48.98 % 64.15 % 71.25 %

Benchmark TP FP FN
CAR 30446 3946 1185
PEDESTRIAN 12357 10793 2431

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 84.77 % 85.08 % 85.08 % 107 71.56 %
PEDESTRIAN 42.01 % 64.57 % 42.88 % 201 23.09 %

Benchmark MT rate PT rate ML rate FRAG
CAR 70.92 % 20.77 % 8.31 % 287
PEDESTRIAN 21.99 % 42.61 % 35.40 % 1134

Benchmark # Dets # Tracks
CAR 31631 804
PEDESTRIAN 14788 340

This table as LaTeX

This figure as: png pdf

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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