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    This study introduces a robust algorithm for estimating stationary pixel duration in crowd analysis. The method uses sparse constraints and a foreground codebook for accurate stationary crowd activity detection and traffic pattern analysis.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Pattern Recognition

    Background:

    • Stationary crowd analysis is crucial for surveillance, complementing mobile group modeling.
    • Accurate estimation of stationary time is challenging due to factors like lighting variations and occlusions.

    Purpose of the Study:

    • To develop a robust algorithm for estimating stationary pixel duration in crowd scenes.
    • To improve the accuracy of stationary time estimation beyond traditional background subtraction methods.

    Main Methods:

    • Proposed a novel algorithm for stationary-time estimation using sparse constraints (L0 optimization) on spatial and temporal dimensions.
    • Incorporated mixed partials (second-order gradients) to create a 3D stationary-time map.
    • Utilized a locally shared foreground codebook to differentiate overlapping or close foreground objects.

    Main Results:

    • The algorithm achieves robust and accurate estimation of stationary pixel duration.
    • Successfully distinguished between different foreground objects in complex spatio-temporal scenarios.
    • Demonstrated effectiveness in analyzing stationary crowd activities, scene structures, and traffic pattern influences.

    Conclusions:

    • The proposed method offers a significant advancement in stationary crowd analysis.
    • The developed techniques have practical applications in crowd surveillance, activity recognition, and traffic management.
    • This work provides a foundation for more sophisticated analysis of crowd dynamics.