Small Group Detection in Crowds using Interaction Information

Kai TAN  Linfeng XU  Yinan LIU  Bing LUO  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.7   pp.1542-1545
Publication Date: 2017/07/01
Publicized: 2017/04/17
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDL8192
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
small group detection,  interaction,  trajectory clustering,  

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Small group detection is still a challenging problem in crowds. Traditional methods use the trajectory information to measure pairwise similarity which is sensitive to the variations of group density and interactive behaviors. In this paper, we propose two types of information by simultaneously incorporating trajectory and interaction information, to detect small groups in crowds. The trajectory information is used to describe the spatial proximity and motion information between trajectories. The interaction information is designed to capture the interactive behaviors from video sequence. To achieve this goal, two classifiers are exploited to discover interpersonal relations. The assumption is that interactive behaviors often occur in group members while there are no interactions between individuals in different groups. The pairwise similarity is enhanced by combining the two types of information. Finally, an efficient clustering approach is used to achieve small group detection. Experiments show that the significant improvement is gained by exploiting the interaction information and the proposed method outperforms the state-of-the-art methods.