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Measuring Crowd Collectiveness.

Bolei Zhou, Xiaoou Tang, Hepeng Zhang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new collectiveness descriptor to quantify how individuals act as a union in crowd systems. This method effectively detects collective motion and can be applied across diverse crowd dynamics.

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    Area of Science:

    • Complex Systems Science
    • Computational Social Science
    • Data Science

    Background:

    • Collective motion is prevalent in nature and studied across disciplines.
    • Quantifying collectiveness, the degree of unified individual action, is crucial for understanding crowd dynamics.
    • Existing methods lack a universal, efficient descriptor for collectiveness.

    Purpose of the Study:

    • To propose a novel, efficient descriptor for quantifying collectiveness in crowd systems.
    • To develop an algorithm for detecting collective motion from random movements.
    • To validate the descriptor's effectiveness and universality across various crowd types.

    Main Methods:

    • Quantification of topological structures within collective manifolds.
    • Development of a collectiveness descriptor and its efficient computation.
    • Introduction of the Collective Merging algorithm for collective motion detection.

    Main Results:

    • The proposed collectiveness descriptor accurately quantifies collective behavior in diverse systems (self-driven particles, pedestrians, bacteria).
    • The Collective Merging algorithm effectively distinguishes collective from random motions.
    • High consistency was observed between the descriptor and human perception of collective motion.

    Conclusions:

    • The proposed collectiveness descriptor is a universal and robust metric for crowd systems.
    • It enables comparison between different crowd dynamics and has broad applications in analysis and monitoring.
    • A new public Collective Motion Database is released to facilitate further research.