Mean shift algorithm based on fusion model for head tracking  

基于融合模板的均值移动头部跟踪算法(英文)

作　　者：安国成[1,2] 高建坡[1] 吴镇扬[1] 

机构地区：[1]东南大学信息科学与工程学院,南京210096 [2]中国科学院软件研究所人机交互技术与智能信息处理实验室,北京100190

出　　处：《Journal of Southeast University(English Edition)》2009年第3期299-302,共4页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.60672094,60673188,U0735004);the National High Technology Research and Development Program of China(863 Program)(No.2008AA01Z303);the National Basic Research Program of China (973 Program)(No.2009CB320804)

摘　　要：To solve the mismatch between the candidate model and the reference model caused by the time change of the tracked head, a novel mean shift algorithm based on a fusion model is provided. A fusion model is employed to describe the tracked head by sampling the models of the fore-head and the back-head under different situations. Thus the fusion head reference model is represented by the color distribution estimated from both the fore- head and the back-head. The proposed tracking system is efficient and it is easy to realize the goal of continual tracking of the head by using the fusion model. The results show that the new tracker is robust up to a 360°rotation of the head on a cluttered background and the tracking precision is improved.针对被跟踪头部目标特征状态随时间变化而与参考模板不匹配的问题,提出一种基于融合参考模板的均值移动算法,即将被跟踪目标在不同状态下所呈现出的不同特征使用采样的方法进行融合,如将头部跟踪过程中正面的肤色信息和后面的发色信息进行融合,从而形成一个包含不同特征的参考模板.在跟踪过程中,使用该融合模板可以有效地克服由被跟踪目标特征变化导致跟踪失败而不能实现头部连续跟踪的问题.通过头部跟踪实验可以看出,该算法实现了复杂环境下的具有360°旋转的头部跟踪,并且在一定程度上提高了跟踪精度.

关 键 词：mean shift head tracking kernel density estimate fusion model 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]