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Computer Science > Computer Vision and Pattern Recognition

arXiv:2411.11922 (cs)
[Submitted on 18 Nov 2024]

Title:SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual
Tracking with Motion-Aware Memory

Authors:Cheng-Yen Yang, Hsiang-Wei Huang, Wenhao Chai, Zhongyu Jiang,
Jenq-Neng Hwang
View a PDF of the paper titled SAMURAI: Adapting Segment Anything
Model for Zero-Shot Visual Tracking with Motion-Aware Memory, by
Cheng-Yen Yang and 4 other authors
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    Abstract:The Segment Anything Model 2 (SAM 2) has demonstrated
    strong performance in object segmentation tasks but faces
    challenges in visual object tracking, particularly when managing
    crowded scenes with fast-moving or self-occluding objects.
    Furthermore, the fixed-window memory approach in the original
    model does not consider the quality of memories selected to
    condition the image features for the next frame, leading to error
    propagation in videos. This paper introduces SAMURAI, an enhanced
    adaptation of SAM 2 specifically designed for visual object
    tracking. By incorporating temporal motion cues with the proposed
    motion-aware memory selection mechanism, SAMURAI effectively
    predicts object motion and refines mask selection, achieving
    robust, accurate tracking without the need for retraining or
    fine-tuning. SAMURAI operates in real-time and demonstrates
    strong zero-shot performance across diverse benchmark datasets,
    showcasing its ability to generalize without fine-tuning. In
    evaluations, SAMURAI achieves significant improvements in success
    rate and precision over existing trackers, with a 7.1% AUC gain
    on LaSOT$_{\text{ext}}$ and a 3.5% AO gain on GOT-10k. Moreover,
    it achieves competitive results compared to fully supervised
    methods on LaSOT, underscoring its robustness in complex tracking
    scenarios and its potential for real-world applications in
    dynamic environments. Code and results are available at this
    https URL.

Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2411.11922 [cs.CV]
          (or arXiv:2411.11922v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2411.11922
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Cheng-Yen Yang [view email]
[v1] Mon, 18 Nov 2024 05:59:03 UTC (24,008 KB)
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    View a PDF of the paper titled SAMURAI: Adapting Segment Anything
    Model for Zero-Shot Visual Tracking with Motion-Aware Memory, by
    Cheng-Yen Yang and 4 other authors
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