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    This study adapts re-identification (re-ID) feature distances for multi-target multi-camera tracking (MTMCT). The new method improves affinity estimation for local matching, enhancing tracking accuracy.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Multi-target multi-camera tracking (MTMCT) often relies on re-identification (re-ID) feature distances for data association.
    • Global matching in re-ID differs significantly from the local matching required in MTMCT.

    Purpose of the Study:

    • To investigate the misfit between global re-ID feature distances and local matching in MTMCT.
    • To propose an adaptive affinity estimation method tailored for MTMCT's local scope.

    Main Methods:

    • Designed experiments to verify the mismatch between global re-ID distances and local tracking needs.
    • Introduced an adaptive affinity module specializing in appearance changes relevant to data association.
    • Implemented a data sampling scheme with temporal windows for localized matching.

    Main Results:

    • The adaptive affinity module significantly improves upon global re-ID distance.
    • The proposed method demonstrates competitive performance on benchmark datasets like CityFlow and DukeMTMC.
    • Tailoring affinity metrics to local matching scopes enhances MTMCT accuracy.

    Conclusions:

    • Global re-ID feature distances are suboptimal for MTMCT data association.
    • Adapting affinity estimation to local matching scopes is crucial for effective MTMCT.
    • The proposed adaptive affinity module offers a more suitable approach for MTMCT.