Measuring Crowd Collectiveness
Measuring Crowd Collectiveness
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DOI：
10.1109/cvpr.2013.392
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Bolei Zhou;Xiaoou Tang;Hepeng Zhang;Xiaogang Wang
中科院分区：
文献类型：
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作者：
Bolei Zhou;Xiaoou Tang;Hepeng Zhang;Xiaogang Wang
群体运动是群体系统中常见的现象，已经引起了多学科领域的广泛关注。群体性是衡量各种群体系统的基本尺度，它反映了群体中个体在集体运动中作为一个整体的程度。通过整合集合流形上群体之间的路径相似性，提出了一种群体性的描述方法，并给出了群体及其组成个体的有效计算方法。然后提出了一种从随机运动中检测集体运动的集体合并算法。我们验证了所提出的集体描述子对自驱动粒子系统的有效性和鲁棒性。然后，我们比较集体性描述符人类感知集体运动，并表现出高度的一致性。我们的实验关于集体运动的检测和测量的集体性在视频中的行人人群和细菌菌落展示了广泛的应用的集体性描述符。
Collective motions are common in crowd systems and have attracted a great deal of attention in a variety of multidisciplinary fields. Collectiveness, which indicates the degree of individuals acting as a union in collective motion, is a fundamental and universal measurement for various crowd systems. By integrating path similarities among crowds on collective manifold, this paper proposes a descriptor of collectiveness and an efficient computation for the crowd and its constituent individuals. The algorithm of the Collective Merging is then proposed to detect collective motions from random motions. We validate the effectiveness and robustness of the proposed collectiveness descriptor on the system of self-driven particles. We then compare the collectiveness descriptor to human perception for collective motion and show high consistency. Our experiments regarding the detection of collective motions and the measurement of collectiveness in videos of pedestrian crowds and bacteria colony demonstrate a wide range of applications of the collectiveness descriptor.