Drone-captured crowd videos have become increasingly prevalent in various applications in recent years, including crowd density estimation via measuring crowd collectiveness. Traditional methods often measure local differences in motion directions among individuals and scarcely handle the challenge brought by the changing illumination of scenarios. They are limited in their generalization. The crowd density estimation needs both macroscopic and microscopic descriptions of collective motion. In this study, we introduce a Global Measuring Crowd Collectiveness (GMCC) metric that incorporates intra-crowd and inter-crowd collectiveness to assess the collective crowd motion. An energy spread process is introduced to explore the related crucial factors. This process measures the intra-crowd collectiveness of individuals within a crowded cluster by incorporating the collectiveness of motion direction and the velocity magnitude derived from the optical flow field. The global metric is adopted to keep the illumination-invariance of optical flow for intra-crowd motion. Then, we measure the motion consistency among various clusters to generate inter-crowd collectiveness, which constitutes the GMCC metric together with intra-collectiveness. Finally, the proposed energy spread process of GMCC is used to merge the inter-crowd collectiveness to estimate the global distribution of dense crowds. Experimental results validate that GMCC significantly improves the performance and efficiency of measuring crowd collectiveness and crowd density estimation on various crowd datasets, demonstrating a wide range of applications for real-time monitoring in public crowd management.


    Access

    Download


    Export, share and cite



    Title :

    Crowd Density Estimation via Global Crowd Collectiveness Metric


    Contributors:
    Ling Mei (author) / Mingyu Yu (author) / Lvxiang Jia (author) / Mingyu Fu (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    DDAD: Detachable Crowd Density Estimation Assisted Pedestrian Detection

    Tang, Wenxiao / Liu, Kun / Shakeel, M. Saad et al. | IEEE | 2023


    Higher-order SVD analysis for crowd density estimation

    Zhou, B. / Zhang, F. / Peng, L. | British Library Online Contents | 2012


    An Efficient Crowd Density Estimation Algorithm Through Network Compression

    Li, Meng / Chen, Tao / Li, Zhihua et al. | TIBKAT | 2020


    Crowd Density Estimation Using Fusion of Multi-Layer Features

    Ding, Xinghao / He, Fujin / Lin, Zhirui et al. | IEEE | 2021


    An Efficient Crowd Density Estimation Algorithm Through Network Compression

    Li, Meng / Chen, Tao / Li, Zhihua et al. | Springer Verlag | 2020