{"title":"Dual Searching Window Based Face Tracking","authors":"Qiang Liu, C. Cai","doi":"10.1109/ISPACS.2006.364886","DOIUrl":null,"url":null,"abstract":"In this paper, an enhanced CAMShift (continuously adaptive mean shift) object-tracking algorithm, called dual searching window based face-tracking algorithm, is proposed. Firstly, the near complexion background is removed from video frames by using a mixture Gaussian model. An accessory window is then introduced to correct the tracking error resulting from temporary occlusion by an object with similar probability distribution. Experimental results have testified the proposed face-tracking scheme can greatly alleviate the flesh-like background interference and miss tracking due to the temporary occlusion","PeriodicalId":178644,"journal":{"name":"2006 International Symposium on Intelligent Signal Processing and Communications","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 International Symposium on Intelligent Signal Processing and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPACS.2006.364886","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, an enhanced CAMShift (continuously adaptive mean shift) object-tracking algorithm, called dual searching window based face-tracking algorithm, is proposed. Firstly, the near complexion background is removed from video frames by using a mixture Gaussian model. An accessory window is then introduced to correct the tracking error resulting from temporary occlusion by an object with similar probability distribution. Experimental results have testified the proposed face-tracking scheme can greatly alleviate the flesh-like background interference and miss tracking due to the temporary occlusion
本文提出了一种增强的CAMShift (continuous adaptive mean shift)目标跟踪算法，即基于双搜索窗口的人脸跟踪算法。首先，利用混合高斯模型从视频帧中去除近肤色背景;在此基础上，引入一个辅助窗口，对具有相似概率分布的物体临时遮挡造成的跟踪误差进行校正。实验结果表明，所提出的人脸跟踪方案能够极大地缓解背景肉样干扰和由于临时遮挡造成的跟踪缺失
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