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Updated: Sep 14, 2025

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360VOTS: Visual Object Tracking and Segmentation in Omnidirectional Videos.

Yinzhe Xu, Huajian Huang, Yingshu Chen

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    Summary
    This summary is machine-generated.

    Researchers developed a new method for tracking and segmenting objects in 360° videos, addressing challenges like wide fields-of-view. The extended bounding field-of-view (eBFoV) representation and a new dataset improve omnidirectional visual object tracking and segmentation.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Omnidirectional videos present unique challenges for object tracking and segmentation due to their wide field-of-view and significant spherical distortion.
    • Existing methods struggle to accurately localize and track objects in 360° imagery.

    Purpose of the Study:

    • To introduce a novel representation and framework for robust visual object tracking and segmentation in omnidirectional videos.
    • To establish a comprehensive dataset and benchmark for evaluating 360° video object segmentation (360VOS) algorithms.

    Main Methods:

    • A new representation, extended bounding field-of-view (eBFoV), was developed for target localization.
    • A general 360 tracking framework was proposed, building upon prior omnidirectional visual object tracking (360VOT) work.
    • A new dataset, 360VOS, comprising 290 sequences with pixel-wise masks, was created and divided into training (170 sequences) and testing (120 sequences) subsets.

    Main Results:

    • The proposed eBFoV representation and 360 tracking framework demonstrate effectiveness for both omnidirectional tracking and segmentation tasks.
    • Extensive experiments benchmark state-of-the-art approaches on the new 360VOS dataset.
    • Tailored evaluation metrics were developed for rigorous assessment of omnidirectional tracking and segmentation performance.

    Conclusions:

    • The novel eBFoV representation and the proposed 360 tracking framework significantly advance omnidirectional visual object tracking and segmentation.
    • The 360VOS dataset and benchmark provide essential resources for future research and development in this domain.
    • The study highlights the effectiveness of the proposed methods and dataset in addressing the complexities of 360° video analysis.