基于轨迹聚类的超市顾客运动跟踪  

Trajectory clustering based customer movement tracking in a supermarket

作　　者：王熙[1] 吴为 钱沄涛[1] 

机构地区：[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

出　　处：《智能系统学报》2015年第2期187-192,共6页CAAI Transactions on Intelligent Systems

基　　金：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)

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。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.

关 键 词：目标跟踪 特征匹配 轨迹聚类 运动分析 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]