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Robust online multiobject tracking with data association and track management.

Seung-Hwan Bae, Kuk-Jin Yoon

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 8, 2014
    PubMed
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    This study introduces an online multiobject tracking system for complex scenes. It robustly tracks objects through occlusions using novel data association, track management, and online model learning, outperforming existing methods.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Robotics

    Background:

    • Multiobject tracking in complex scenes presents challenges due to occlusions and dynamic environments.
    • Existing batch tracking systems process entire sequences, limiting real-time applications.
    • Online tracking systems require robust methods for sequential data association and track management.

    Purpose of the Study:

    • To develop a novel online multiobject tracking system for robust performance in complex scenes.
    • To address challenges posed by frequent and long-term occlusions in object tracking.
    • To improve sequential track building using online-provided detections.

    Main Methods:

    • A novel data association method incorporating track existence probability for robust association under partial occlusions.

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  • A track management component to re-identify and link fragmented tracks caused by long-term occlusions.
  • Online appearance model learning to generate discriminative features for accurate object association.

    • Main Results:

    • The proposed online multiobject tracking system demonstrates significant performance improvements over state-of-the-art methods on challenging public datasets.
    • Experimental results validate the effectiveness of the novel data association, track management, and online model learning components.
    • The system achieves robust object tracking even under frequent and prolonged occlusions.

    Conclusions:

    • The developed online multiobject tracking system offers a robust and effective solution for complex tracking scenarios.
    • The integrated approach of data association, track management, and online learning is crucial for handling occlusions.
    • The system's modular design allows for analysis of individual component contributions to overall tracking performance.