Method

FGO-Test [FGO-Test]
no

Submitted on 4 Jun. 2026 09:27 by
shq feng (Wuhan University)

Running time:0.3 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
The proposed method is a LiDAR-based 3D multi-
object tracking framework that combines deep
motion prediction and factor graph
optimization.The framework produces smooth and
consistent trajectories while maintaining real-
time performance on KITTI sequences.
Parameters:
no
Latex Bibtex:
no

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 90.18 % 87.35 % 90.25 % 90.09 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.09 % 97.30 % 95.67 % 37002 1027 2326 9.23 % 42517 776

Benchmark MT PT ML IDS FRAG
CAR 83.38 % 11.69 % 4.92 % 23 315

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