Method

SAM2-based Multi-object Tracking and Segmentation using Zero-shot Learning [Seg2Track-SAM2]
[Anonymous Submission]

Submitted on 9 Sep. 2025 18:15 by
[Anonymous Submission]

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:

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 60.42 % 54.92 % 67.95 % 63.13 % 68.64 % 72.40 % 81.86 % 78.93 %
PEDESTRIAN 44.41 % 39.48 % 50.51 % 49.28 % 53.91 % 55.98 % 66.97 % 73.83 %

Benchmark TP FP FN
CAR 26504 7888 5124
PEDESTRIAN 15106 8044 6058

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 61.60 % 75.93 % 62.17 % 193 43.05 %
PEDESTRIAN 37.81 % 69.31 % 39.08 % 296 17.78 %

Benchmark MT rate PT rate ML rate FRAG
CAR 59.38 % 34.15 % 6.46 % 820
PEDESTRIAN 36.43 % 44.33 % 19.24 % 1059

Benchmark # Dets # Tracks
CAR 31628 801
PEDESTRIAN 21164 374

This table as LaTeX

This figure as: png pdf

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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