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 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 79.35 % 76.64 % 82.81 % 83.18 % 83.59 % 86.55 % 90.02 % 87.81 %

Benchmark TP FP FN
CAR 32314 2078 1907

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 88.24 % 86.54 % 88.41 % 60 75.60 %

Benchmark MT rate PT rate ML rate FRAG
CAR 84.77 % 13.08 % 2.15 % 243

Benchmark # Dets # Tracks
CAR 34221 816

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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