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Adaptive Clustering Algorithm based small Group Detection and Tracking
CHENG Zhongbin #,ZOU Qi *,TIAN Mei
School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044
*Correspondence author
#Submitted by
Subject:
Funding: The National Natural Science Foundation of China（No.NO.61473031）, Fundamental Research Funds for the Central Universities （No.No.2015JBM036, NO.2016JBM016)）
Opened online: 9 September 2016
Accepted by: none
Citation: CHENG Zhongbin,ZOU Qi,TIAN Mei.Adaptive Clustering Algorithm based small Group Detection and Tracking[OL]. [ 9 September 2016] http://en.paper.edu.cn/en_releasepaper/content/4703372
 
 
We propose to detect and track small groups of individuals who are traveling together in surveiuance videos. Coherent groups are dynamically updated through merge and split events. To handle these challenges, we propose to discover groups by adaptive clustering (AGD). Experiments on challenging videos (FM dataset) which have complex motions and occlusions show that the proposed method based on trajectory-level similarity can correctly discover dynamic changes of groups. The effectiveness of the proposed approach is shown through comparison with classical methods.
Keywords:Small group; Data Association; Similarity measure；clustering; adaptive
 
 
 

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