Method

Memory-Augmented Segment Anything Model with Identity Consistency for MOTS [MASAM]
[Anonymous Submission]

Submitted on 7 Jul. 2025 10:06 by
[Anonymous Submission]

Running time:0.44 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
We propose a memory-augmented framework that adapts
SAM2 for performing identity-aware multi-object
tracking and segmentation without the need for fine-
tuning.
Parameters:
TBD
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 77.02 % 76.49 % 78.14 % 81.13 % 86.47 % 82.21 % 89.11 % 88.20 %
PEDESTRIAN 63.64 % 64.17 % 64.75 % 71.49 % 74.82 % 69.28 % 82.12 % 81.78 %

Benchmark TP FP FN
CAR 33463 3297 1026
PEDESTRIAN 18122 2575 1655

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
CAR 87.89 % 86.91 % 88.24 % 130 75.97 %
PEDESTRIAN 78.89 % 79.10 % 79.56 % 140 60.59 %

Benchmark MT rate PT rate ML rate FRAG
CAR 76.43 % 19.52 % 4.05 % 566
PEDESTRIAN 68.89 % 25.56 % 5.56 % 539

Benchmark # Dets # Tracks
CAR 34489 759
PEDESTRIAN 19777 419

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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