An Efficient Method of Crowd Aggregation Computation in Public Areas
An Efficient Method of Crowd Aggregation Computation in Public Areas
复制标题
一种高效的公共区域人群聚集计算方法
DOI：
10.1109/tcsvt.2017.2731866
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发表时间：
2018-10
影响因子：
8.4
通讯作者：
Zhou Bing
中科院分区：
文献类型：
--
作者：
Xu Mingliang;Li Chunxu;Lv Pei;Lin Nie;Hou Rui;Zhou Bing
由于城市人口和规模的迅速增加，公共区域的人群踩踏和恐怖袭击现在已经成为更加严重和危险的威胁。因此，对人群聚集行为的分析成为智能视频监控领域的一个新的研究热点。然而，这样的公共区域场景不仅包含移动的人群，而且还包含其他类型的对象。这些物体的尺寸通常很小，这使得它们的外观非常相似。此外，群体中的个体随机移动，并且经常相互遮挡。所有这些因素使得人群聚集的分析变得非常困难。本文试图从三个方面解决这一问题。首先，一个新的全球功能被用来表示移动人群。该特征能够很好地描述感兴趣点的空间和时间运动信息。其次，采用先对特征点进行聚类，再计算集合度的策略。这使得单个组的集体计算更加一致和有效。最后，提出了更全面的集体人群描述符，以提供对人群状态的详细描述。基于该描述子，实现了群体运动的演化分析和人群异常检测。实验结果表明，该方法能够有效地计算不同公共区域的人群聚集度，为公共安全管理提供了可靠的参考依据。
The crowd stampede and terrorist attacks in public areas have now become more serious and dangerous threats due to the rapid increase in the population and scale of cities. Therefore, the analysis of crowd aggregation behavior has been a new research focus in the field of intelligent video surveillance. However, such public area scenes not only contain moving crowd but also contain other types of objects. The sizes of these objects are usually small, which make their appearances quite similar. Moreover, the individuals in a crowd move randomly and often occlude each other. All the above factors make the analysis of crowd aggregation very difficult. In this paper, the authors attempt to solve this problem in three aspects. First, a novel global feature is used to represent the moving crowd. This feature can well describe the spatial and the temporal motion information of points-of-interest. Second, a strategy is adopted to cluster the feature points first and then calculate the collectiveness. This makes the collectiveness computation of individual groups more consistent and effective. Finally, more comprehensive collective crowd descriptors are proposed to provide a detailed description of the crowd status. Based on the proposed descriptor, the authors realize the evolution analysis of the group movement and the crowd abnormal detection. The experiment results show that the proposed method is able to efficiently compute the crowd collectiveness in various public areas and provide a reliable reference for the public safety management.
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DOI：
10.1109/tkde.2004.1269669
发表时间：
2004-04
影响因子：
8.9
作者：
C. Aggarwal
通讯作者：
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影响因子：
19
作者：
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通讯作者：
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DOI：
10.1007/978-3-319-02447-9_1
发表时间：
2014
期刊：
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影响因子：
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作者：
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DOI：
10.1109/34.868688
发表时间：
2000-08-01
影响因子：
23.6
作者：
Shi, JB;Malik, J
通讯作者：
Malik, J
DOI：
10.1109/cvpr.2013.392
发表时间：
2013-06
期刊：
2013 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
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作者：
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Bolei Zhou;Xiaoou Tang;Hepeng Zhang;Xiaogang Wang