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Method

Graph-enhanced PMB(PointGNN) [la] [on] [GePMB]
https://github.com/PeterXu0124/GePMB

Submitted on 14 Sep. 2026 05:05 by
cao cao (cao)

Running time:0.20 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Graph-enhanced PMB is a LiDAR-based online 3D
multi-object tracking method built upon the
Poisson Multi-Bernoulli filtering framework. A
graph neural network is introduced to learn
discriminative affinities between predicted tracks
and current detections, improving data association
in crowded and ambiguous scenes. In addition, a
neural-enhanced motion model combines a CTRA
physical prior with recurrent residual prediction
to better handle nonlinear target motion. A
lifecycle-aware management strategy is further
employed to improve track initialization, missed-
detection handling, and termination. The method
preserves probabilistic PMB inference while
enhancing association, motion prediction, and
track management with learned components.
Parameters:
\alpha=0.2
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 78.51 % 84.99 % 78.71 % 89.04 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 80.89 % 98.17 % 88.70 % 28724 534 6788 4.80 % 32603 580

Benchmark MT PT ML IDS FRAG
CAR 64.15 % 12.15 % 23.69 % 69 455

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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