[1]王熙,吴为,钱沄涛.基于轨迹聚类的超市顾客运动跟踪[J].智能系统学报,2015,10(02):187-192.[doi:10.3969/j.issn.1673-4785.201401002]
 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]
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基于轨迹聚类的超市顾客运动跟踪(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年02期
页码:
187-192
栏目:
出版日期:
2015-04-25

文章信息/Info

Title:
Trajectory clustering based customer movement tracking in a supermarket
作者:
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
Author(s):
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
关键词:
目标跟踪特征匹配轨迹聚类运动分析
Keywords:
object trackingfeature matchingtrajectory clusteringfeature point refining
分类号:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201401002
文献标志码:
A
摘要:
针对超市等复杂应用环境下的运动目标轨迹跟踪问题,将轨迹聚类运用于目标跟踪中,提出了一种超市顾客运动跟踪方法。该方法对Kanade-Lucas-Tomasi(KLT)算法提取并跟踪得到的特征点轨迹进行预处理,滤除背景和短时特征点以分离出运动目标所在区域的关键特征点;进而采用均值漂移(meanshift)算法进行轨迹聚类,解决了单帧静态特征点聚类时的目标遮挡问题;最后采用运动跟踪匹配算法对前后帧的特征点进行最优匹配,解决了目标出入视频区域以及具有复杂路线时的稳定跟踪问题,得到顾客的完整运动轨迹。实验结果表明,该方法能够在超市入口、生鲜区以及收银台等各种典型超市区域中完成顾客轨迹的运动跟踪,并对顾客部分遮挡、复杂运动轨迹以及异步运动等多种特殊情况具有较高的鲁棒性。
Abstract:
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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-1-13;改回日期:。
基金项目:国家科技支撑计划资助项目(2011BAD24B03)浙江省网络系统及信息安全重点实验室开放基金资助项目(2013002).
作者简介:王熙,男,1989年生,硕士研究生,主要研究方向为计算机视觉和数据挖掘;吴为,1961年生,高级工程师,主要研究方向为计算机应用,发表学术论文10余篇;钱沄涛,1968年生,教授,博士生导师,中国电子学会高级会员,信号处理分会委员;中国计算机学会人工智能与模式识别专委会委员,中国计算机学会模糊逻辑与多值逻辑专委会委员;中国航空学会信息融合分会委员;中国人工智能学会智能CAD与数字艺术专委会委员。主要研究方向为模式识别、信号处理、机器学习,发表学术论文90余篇。
通讯作者:钱沄涛.E-mail:ytqian@zju.edu.cn.
更新日期/Last Update: 2015-06-15