{"title":"Pairwise Threshold for Gaussian Mixture Classification and Its Application on Human Tracking Enhancement","authors":"Daegeon Kim, S. Lee","doi":"10.1109/AVSS.2012.53","DOIUrl":null,"url":null,"abstract":"In this paper, we describe Object Pixel Mixture Classifiers (OPMCs) which classify an object not only apart from background but also from other objects based on Gaussian Mixture Model (GMM) classification. The proposed OPMC is different from general GMM based classifiers in the respect that novel pairwise threshold is applied for final classification. Pairwise thresholds are different thresholds depending on predicted mixture component index combination by a positive and a negative GMMs. We train the pairwise threshold using discriminative model so that generative GMM can take advantage from it. We demonstrate that OPMCs are robust to noise in train data and can keep tracking objects after missing tracks even with occlusion. Also, we show that OPMCs can generate meaningful blob of object, and can separate the region of objects from merged blobs.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AVSS.2012.53","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we describe Object Pixel Mixture Classifiers (OPMCs) which classify an object not only apart from background but also from other objects based on Gaussian Mixture Model (GMM) classification. The proposed OPMC is different from general GMM based classifiers in the respect that novel pairwise threshold is applied for final classification. Pairwise thresholds are different thresholds depending on predicted mixture component index combination by a positive and a negative GMMs. We train the pairwise threshold using discriminative model so that generative GMM can take advantage from it. We demonstrate that OPMCs are robust to noise in train data and can keep tracking objects after missing tracks even with occlusion. Also, we show that OPMCs can generate meaningful blob of object, and can separate the region of objects from merged blobs.