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 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 91.52 % 86.92 % 91.59 % 89.61 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.86 % 98.74 % 96.24 % 37009 472 2421 4.24 % 42301 671

Benchmark MT PT ML IDS FRAG
CAR 86.62 % 5.23 % 8.15 % 23 79

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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