Method

Object-Centric Appearance Estimation for Multi-Object Tracking [on] [Polycepta]


Submitted on 12 Feb. 2026 12:41 by
Mohamed Mostafa (Khalifa University)

Running time:0.11 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Appearance estimation in multi-object tracking.
Parameters:
TBA
Latex Bibtex:
@misc{nagy2026polyceptaobjectcentricappearanceestimation,
title={Polycepta: Object-Centric Appearance Estimation for
Multi-Object Tracking},
author={Mohamed Nagy and Naoufel Werghi and Jorge Dias
and Majid Khonji},
year={2026},
eprint={2606.23604},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2606.23604},
}

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 81.35 % 78.95 % 84.41 % 84.26 % 85.48 % 87.74 % 90.44 % 87.78 %

Benchmark TP FP FN
CAR 32774 1618 1127

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.80 % 86.39 % 92.02 % 76 78.83 %

Benchmark MT rate PT rate ML rate FRAG
CAR 89.08 % 7.69 % 3.23 % 423

Benchmark # Dets # Tracks
CAR 33901 681

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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