Adaptive topology learning of camera network across non-overlapping views  

无重叠视域摄像机网络拓扑自适应学习方法(英文)

作　　者：杨彪[1] 林国余[2] 张为公[2] 

机构地区：[1]常州大学信息科学与工程学院,常州213164 [2]东南大学仪器科学与工程学院,南京210096

出　　处：《Journal of Southeast University(English Edition)》2015年第1期61-66,共6页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.60972001);the Science and Technology Plan of Suzhou City(No.SS201223)

摘　　要：An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is judged by their cross-correlation function, which is also used to calculate their transition time distribution. The mutual information of the connected node pair is employed for transition probability calculation. A false link eliminating approach is proposed, along with a topology updating strategy to improve the learned topology. A real monitoring system with five disjoint cameras is built for experiments. Comparative results with traditional methods show that the proposed method is more accurate in topology learning and is more robust to environmental changes.提出一种自适应拓扑学习方法来无监督地学习摄像机网络的拓扑.利用混合高斯算法建立节点模型,并通过计算节点对的互关联函数得到该对节点的连通性以及连通节点对的转移时间分布.利用交互信息计算连通的节点对的转移概率.对学习到的拓扑结构,提出虚假连接排除策略以及拓扑更新策略对其进行优化.为了测试所提出算法的有效性,搭建了由5个不包含重叠视域的摄像机组成的监控系统进行试验.通过与已有算法的对比,结果表明该算法可以更准确地学习监控网络的拓扑,并对环境变化有一定的鲁棒性.

关 键 词：non-overlapping views mutual information Gaussian mixture model adaptive topology learning cross-correlation function 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]