Method

SAM2-based Multi-object Tracking and Segmentation using Zero-shot Learning [Seg2Track-SAM2]
https://github.com/hcmr-lab/Seg2Track-SAM2

Submitted on 9 Sep. 2025 18:15 by
Diogo Mendonça (Universidade de Coimbra)

Running time:1 s
Environment:GPU @ 1.5 Ghz (Python)

Method Description:
This method extends SAM2 to multi-object tracking
and segmentation in a zero-shot setting. Objects are
initialized with a detector and refined over time
through object reinforcement, ensuring consistent
masks across frames without extra training.
Parameters:
\detection_threshold=0.5
\removal_threshold=0.1
Latex Bibtex:
@misc{mendonça2025seg2tracksam2sam2basedmultiobjecttracking,
title={Seg2Track-SAM2: SAM2-based Multi-object Tracking and Segmentation for Zero-shot Generalization},
author={Diogo Mendonça and Tiago Barros and Cristiano Premebida and Urbano J. Nunes},
year={2025},
eprint={2509.11772},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2509.11772},
}

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 61.52 % 76.66 % 62.23 % 81.01 %
PEDESTRIAN 37.48 % 69.41 % 39.10 % 90.45 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 79.62 % 85.75 % 82.58 % 30780 5113 7877 45.96 % 47510 989
PEDESTRIAN 65.80 % 71.60 % 68.58 % 15385 6103 7995 54.86 % 27345 417

Benchmark MT PT ML IDS FRAG
CAR 59.54 % 34.46 % 6.00 % 244 849
PEDESTRIAN 36.08 % 45.02 % 18.90 % 376 1271

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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