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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Data Science

    Background:

    • Detecting coherent groups is crucial for crowd behavior analysis.
    • Existing methods often suffer from limited utilization of crowd properties and arbitrary individual processing.

    Purpose of the Study:

    • To propose a novel Multiview-based Parameter Free (MPF) framework for automated group detection in crowd analysis.
    • To address limitations in current crowd behavior analysis techniques by enhancing feature utilization and processing.

    Main Methods:

    • Developed a new structural context descriptor to characterize individual properties in crowd scenes.
    • Proposed a self-weighted multiview clustering method integrating orientation and context similarities.
    • Introduced a parameter-free framework for automatic group number determination.

    Main Results:

    • The MPF framework demonstrated promising performance in group detection on real-world crowd videos.
    • The multiview clustering method showed superiority over state-of-the-art methods on synthetic and benchmark datasets.

    Conclusions:

    • The proposed MPF framework offers an effective and automated solution for group detection in crowd analysis.
    • The developed multiview clustering approach significantly advances the state-of-the-art in feature point clustering.