Method

Offline-Poly: A Polyhedral Framework For Offline 3D Multi-Object [Offline-Poly]
https://github.com/K544-AD/Offline-Poly

Submitted on 21 Feb. 2026 07:56 by
Offline-Poly Team (Harbin Institute of Technology)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
We propose Offline-Poly, a robust and general
framework for offline 3D MOT built upon a novel
Tracking-By-Tracking paradigm.
Parameters:
TBD
Latex Bibtex:
@article{li2026offline,
title={Offline-Poly: A Polyhedral Framework For
Offline 3D Multi-Object Tracking},
author={Li, Xiaoyu and Wu, Yitao and Wu, Xian and
Zhuo, Haolin and Zhao, Lijun and Sun, Lining},
journal={arXiv preprint arXiv:2602.13772},
year={2026}
}

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 83.00 % 80.45 % 86.27 % 85.72 % 85.57 % 89.79 % 90.60 % 87.88 %

Benchmark TP FP FN
CAR 33268 1124 1182

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 93.19 % 86.59 % 93.30 % 36 80.22 %

Benchmark MT rate PT rate ML rate FRAG
CAR 91.69 % 6.00 % 2.31 % 70

Benchmark # Dets # Tracks
CAR 34450 693

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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