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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Operations Research

    Background:

    • Object tracking is crucial for various applications, but challenges remain in handling interactions and occlusions.
    • Simultaneous tracking of multiple, diverse objects, especially those with indirect evidence, is an open problem.

    Purpose of the Study:

    • To develop a unified framework for tracking diverse, interacting objects using a network-flow formulation.
    • To enable the tracking of occluded or indirectly detected objects by leveraging contextual information from other tracked entities.

    Main Methods:

    • Formulating multi-object tracking as a network-flow mixed integer program.
    • Utilizing intertwined flow variables and linear constraints for simultaneous object tracking.
    • Implementing a tracklet-based approach for efficient real-time performance.

    Main Results:

    • Successfully tracked interacting objects, including cars, pedestrians, carried bags, and sports balls.
    • Demonstrated the ability to infer and track objects not initially detected via direct image evidence.
    • Achieved real-time tracking performance through the tracklet-based implementation.

    Conclusions:

    • The proposed network-flow approach provides a powerful and unified method for complex multi-object tracking scenarios.
    • Jointly estimating object trajectories enhances detection and tracking accuracy, particularly for occluded or indirectly observed objects.
    • The method's versatility is shown across diverse real-world examples, highlighting its practical applicability.