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Toward Accurate Pixelwise Object Tracking via Attention Retrieval.

Zhipeng Zhang, Yufan Liu, Bing Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 7, 2021
    PubMed
    Summary
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    This study introduces a novel unified framework for pixelwise object tracking, enhancing segmentation accuracy by suppressing background clutter. The approach achieves state-of-the-art results on benchmarks while maintaining real-time performance.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Pixelwise object tracking faces challenges balancing speed and segmentation accuracy.
    • Existing real-time methods often reuse backbone features, leading to segmentation inaccuracies due to background clutter.
    • This can result in false positives and reduced segmentation performance.

    Purpose of the Study:

    • To propose a unified framework for accurate pixelwise object tracking and segmentation.
    • To mitigate the negative impact of background clutter on segmentation accuracy.
    • To achieve state-of-the-art performance in real-time pixelwise tracking.

    Main Methods:

    • A unified tracking-retrieval-segmentation framework is proposed, comprising an attention retrieval network (ARN) and an iterative feedback network (IFN).

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  • ARN builds a look-up-table (LUT) from the first frame to generate a target-aware attention map, suppressing background noise.
  • IFN iteratively refines segmentation contours using predicted masks as feedback, enhancing features across resolutions.

    • Main Results:

    • The proposed framework achieves state-of-the-art performance on the VOT2020 benchmark.
    • It operates at a real-time speed of 40 frames per second.
    • The model significantly surpasses SiamMask on VOT2020, DAVIS2016, and DAVIS2017 benchmarks.

    Conclusions:

    • The unified tracking-retrieval-segmentation framework effectively improves pixelwise object tracking accuracy.
    • The attention retrieval and iterative feedback mechanisms successfully address background clutter issues.
    • The approach sets a new state of the art in real-time pixelwise tracking and segmentation.