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 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 91.76 % 86.86 % 91.81 % 89.58 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.17 % 98.64 % 96.35 % 37191 513 2302 4.61 % 42770 668

Benchmark MT PT ML IDS FRAG
CAR 87.23 % 4.46 % 8.31 % 20 76

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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