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    This study introduces a new method for long-term multi-person tracking using online learning and network flow optimization. It effectively associates track fragments, preventing identity switches and recovering missed detections, even in challenging scenarios.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Multi-person tracking is crucial for analyzing complex scenes.
    • Existing methods struggle with occlusions and identity switches.
    • Long-term tracking requires robust association of track fragments.

    Purpose of the Study:

    • To develop a novel method for tracklet association in long-term multi-person tracking.
    • To improve tracking accuracy by preventing identity switches and recovering missed detections.
    • To effectively handle occlusions and object interactions using learned cues.

    Main Methods:

    • Utilizing online target-specific metric learning for appearance cues.
    • Employing coherent dynamics estimation for motion cues.
    • Applying network flow optimization for tracklet association.
    • Learning cue weights to manage challenging tracking situations.

    Main Results:

    • The proposed framework effectively associates track fragments.
    • It demonstrates robustness against occlusions and object interactions.
    • The method outperforms several state-of-the-art tracking approaches on public datasets.

    Conclusions:

    • The novel method enhances long-term multi-person tracking performance.
    • Online learning of appearance and motion cues improves robustness.
    • Network flow optimization provides an effective solution for tracklet association.