Method

PMB-FGO [PMB-FGO]


Submitted on 4 Nov. 2025 19:03 by
Jingyi Jin (Jilin University)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
To achieve both robust uncertainty handling and
temporally consistent estimation in 3D multi-object
tracking (MOT), we propose a unified framework that
integrates random finite set (RFS) theory with
factor graph optimization (FGO).
Parameters:
No
Latex Bibtex:

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 88.35 % 86.59 % 88.48 % 89.25 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.77 % 95.18 % 94.98 % 37446 1895 2066 17.04 % 43989 964

Benchmark MT PT ML IDS FRAG
CAR 84.77 % 13.23 % 2.00 % 45 270

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.


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