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 commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 85.04 % 85.13 % 85.09 % 88.11 %
PEDESTRIAN 42.32 % 64.89 % 43.33 % 89.69 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 89.77 % 96.69 % 93.11 % 34634 1184 3945 10.64 % 39512 926
PEDESTRIAN 54.05 % 84.15 % 65.82 % 12634 2379 10741 21.39 % 15911 365

Benchmark MT PT ML IDS FRAG
CAR 70.92 % 20.77 % 8.31 % 15 256
PEDESTRIAN 21.99 % 42.61 % 35.40 % 233 1141

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