NDLI: Formation Period Matters: Towards Socially Consistent Group Detection via Dense Subgraph Seeking

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Formation Period Matters: Towards Socially Consistent Group Detection via Dense Subgraph Seeking

Content Provider	ACM Digital Library
Author	Zhang, Shengping Zhang, Yanhao Qin, Lei Huang, Qingming Yao, Hongxun
Abstract	Group detection becomes an important task in crowd behavior surveillance. However, most existing methods ignore the formation persistency characteristics, which predict unreliable interactions when the crowd is realistic and complex. To address this issue, we propose a novel graph-based method to declare that the formation period really matters for detecting social groups in crowd. First, we develop a socially motivated representation by modeling the formation period probability in a Bayesian manner, which results in social and temporal consistency for group member interactions. A graph is then established using individuals as nodes and formation periods as edge weights to reflect pedestrian relationships. In this way, seeking of socially consistent groups is converted into an optimization problem which seeks dense subgraphs with maximum formation likelihood within the graph structure. We employ graph shift optimization to detect groups by finding all the dense subgraphs due to its robust performance. In the experimental results on public datasets, our proposed method clearly outperforms other related state-of-the-art methods.
Starting Page	475
Ending Page	478
Page Count	4
File Format	PDF
ISBN	9781450332743
DOI	10.1145/2671188.2749305
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dense subgraph seeking Social group Formation period
Content Type	Text
Resource Type	Article

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