Method

HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking [HybridTrack]


Submitted on 23 Mar. 2025 00:05 by
Leandro Di Bella (ETRO VUB)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Tracking-by-detection paradigm.
Parameters:
Virconv detections
Latex Bibtex:
@misc{dibella2025hybridtrackhybridapproachrobust,
title={HybridTrack: A Hybrid Approach for Robust Multi-Object
Tracking},
author={Leandro Di Bella and Yangxintong Lyu and Bruno
Cornelis and Adrian Munteanu},
year={2025},
eprint={2501.01275},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.01275},
}

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 82.49 % 79.16 % 86.56 % 82.59 % 87.56 % 89.50 % 91.34 % 88.07 %

Benchmark TP FP FN
CAR 31963 2429 479

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.51 % 86.80 % 91.55 % 13 79.24 %

Benchmark MT rate PT rate ML rate FRAG
CAR 86.61 % 5.23 % 8.15 % 86

Benchmark # Dets # Tracks
CAR 32442 631

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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