Huafeng Wang, Yunhong Wang, Zhaoxiang Zhang, Fan Wang, Jin Huang
{"title":"A multi-faces tracking and recognition framework for surveillance system","authors":"Huafeng Wang, Yunhong Wang, Zhaoxiang Zhang, Fan Wang, Jin Huang","doi":"10.1109/IVSURV.2011.6157035","DOIUrl":null,"url":null,"abstract":"A novel framework for unsupervised multi-faces tracking and recognition is built on Detection-Tracking-Recognition (DTR) approach. This framework proposed a hybrid face detector for real-time face tracking which is robust to occlusions and posture changes. Faces acquired during unsupervised detection stage will be further processed by SIFT operator in order to cluster face sequence into certain groups. After that, the relevant faces are put together which is of much importance for face recognition in videos. The framework is validated on several videos collected in unconstrained condition (20min each.).The framework can track the face and automatically group a serial faces for a single human-being object in an unlabeled video robustly.","PeriodicalId":141829,"journal":{"name":"2011 Third Chinese Conference on Intelligent Visual Surveillance","volume":"82 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Third Chinese Conference on Intelligent Visual Surveillance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVSURV.2011.6157035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
A novel framework for unsupervised multi-faces tracking and recognition is built on Detection-Tracking-Recognition (DTR) approach. This framework proposed a hybrid face detector for real-time face tracking which is robust to occlusions and posture changes. Faces acquired during unsupervised detection stage will be further processed by SIFT operator in order to cluster face sequence into certain groups. After that, the relevant faces are put together which is of much importance for face recognition in videos. The framework is validated on several videos collected in unconstrained condition (20min each.).The framework can track the face and automatically group a serial faces for a single human-being object in an unlabeled video robustly.