Learning the three factors of a non-overlapping multi-camera network topology

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	Learning the three factors of a non-overlapping multi-camera network topology
	Xiaotang Chen; Kaiqi Huang; Tieniu Tan
	2012
会议名称	Chinese Conference on Pattern Recognition
会议录名称	2012 5th Chinese Conference on Pattern Recognition
页码	104–112
会议日期	2012
会议地点	China
摘要	In 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.
关键词	Topology Recovering transition Time Distribution camera Network
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12687
专题	智能感知与计算研究中心
通讯作者	Kaiqi Huang
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 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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