Method

HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking [HybridTrack]
https://github.com/leandro-svg/HybridTrack.git

Submitted on 24 May. 2025 21:21 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:
@article{dibella2025hybridtrack,
title={HybridTrack: A Hybrid Approach for Robust
Multi-Object Tracking},
author={Di Bella, Leandro and Lyu, Yangxintong and
Cornelis, Bruno and Munteanu, Adrian},
journal={IEEE Robotics and Automation Letters},
year={2025},

publisher={IEEE}
}

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 82.72 % 79.28 % 86.92 % 82.84 % 87.39 % 89.97 % 91.24 % 88.04 %

Benchmark TP FP FN
CAR 32082 2310 520

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.75 % 86.80 % 91.77 % 8 79.43 %

Benchmark MT rate PT rate ML rate FRAG
CAR 87.23 % 4.46 % 8.31 % 85

Benchmark # Dets # Tracks
CAR 32602 629

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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