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Learning the three factors of a non-overlapping multi-camera network topology
Xiaotang Chen; Kaiqi Huang; Tieniu Tan
2012
Conference NameChinese Conference on Pattern Recognition
Source Publication2012 5th Chinese Conference on Pattern Recognition
Pages104–112
Conference Date2012
Conference PlaceChina
AbstractIn this paper, we propose an unsupervised approach for learning the three factors of the topology of a non-overlapping multi-camera network, which are nodes, links, and transition time distributions. It is a cross-correlation based method. Different from previous methods, the proposed method can deal with large amounts of data without considering the size of time window. The connectivity between nodes is estimated based on the N-neighbor accumulated cross-correlations, as well as the transition time distribution for each link. Furthermore, integrated with similarity cues, the proposed method can be extended into weighted cross-correlation models for better performance. Experimental results both on simulated and real-life datasets demonstrate the effectiveness of the proposed method.
KeywordTopology Recovering   transition Time Distribution   camera Network 
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12687
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Xiaotang Chen,Kaiqi Huang,Tieniu Tan. Learning the three factors of a non-overlapping multi-camera network topology[C],2012:104–112.
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