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 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 91.86 % 86.45 % 92.03 % 88.69 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 95.80 % 97.04 % 96.42 % 36890 1125 1616 10.11 % 45394 784

Benchmark MT PT ML IDS FRAG
CAR 89.08 % 7.69 % 3.23 % 57 424

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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