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 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 81.22 % 78.62 % 84.54 % 83.28 % 86.56 % 87.68 % 91.25 % 88.44 %

Benchmark TP FP FN
CAR 32056 2336 1037

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.14 % 87.28 % 90.19 % 18 78.29 %

Benchmark MT rate PT rate ML rate FRAG
CAR 83.23 % 11.54 % 5.23 % 341

Benchmark # Dets # Tracks
CAR 33093 669

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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