WANG Xi,WU Wei,QIAN Yuntao.Trajectory clustering based customer movement tracking in a supermarket[J].CAAI Transactions on Intelligent Systems,2015,10(02):187-192.[doi:10.3969/j.issn.1673-4785.201401002]





Trajectory clustering based customer movement tracking in a supermarket
王熙1 吴为2 钱沄涛1
1. 浙江大学 计算机科学与技术学院, 浙江 杭州 310027;
2. 浙江省网络系统及信息安全重点实验室, 浙江 杭州 310006
WANG Xi1 WU Wei2 QIAN Yuntao1
1. College of Computer Science, Zhejiang University, Hangzhou 310027, China;
2. Zhejiang Key Laboratory of Network Technology and Information Security, Hangzhou 310006, China
object trackingfeature matchingtrajectory clusteringfeature point refining
Tracking the moving targets in complex scenarios such as supermarkets can be a challenging task. This paper proposes a method to track moving customers in a supermarket by clustering the trajectories of the targets. In this method, all the background and short-time feature points are removed in the preprocessing step in order to refine the feature points, which were detected and tracked by the Kanade-Lucas-Tomasi (KLT) algorithm. The occlusion problem of single frame static feature point clustering is solved by applying the mean shift algorithm to the trajectories of moving objects. Finally, the full trajectories of moving customers are generated by the matching algorithm of movement tracking. The algorithm tackles the stable tracking problem by optimally matching the feature point clusters between successive frames when the target goes across the boundary of the video region or has a complex trajectory. Experimental results showed that the proposed method can successfully track the trajectories of customers in various typical regions of the supermarket such as entrance, fresh area and checkout stand. This method is robust under partial occlusion, complex trajectory and asynchronous moving.


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更新日期/Last Update: 2015-06-15