{"title":"Efficient acquisition of human existence priors from motion trajectories","authors":"H. Habe, Hidehito Nakagawa, M. Kidode","doi":"10.2197/ipsjtcva.2.145","DOIUrl":null,"url":null,"abstract":"This paper reports a method for acquiring the prior probability of human existence by using past human trajectories and the color of an image. The priors play important roles in human detection as well as in scene understanding. The proposed method is based on the assumption that a person can exist again in an area where he/she existed in the past. In order to acquire the priors efficiently, a high prior probability is assigned to an area having the same color as past human trajectories. We use a particle filter for representing the prior probability. Therefore, we can represent a complex prior probability using only a few parameters. Through experiments, we confirmed that our proposed method can acquire the prior probability efficiently and it can realize highly accurate human detection using the obtained prior probability.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2197/ipsjtcva.2.145","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
This paper reports a method for acquiring the prior probability of human existence by using past human trajectories and the color of an image. The priors play important roles in human detection as well as in scene understanding. The proposed method is based on the assumption that a person can exist again in an area where he/she existed in the past. In order to acquire the priors efficiently, a high prior probability is assigned to an area having the same color as past human trajectories. We use a particle filter for representing the prior probability. Therefore, we can represent a complex prior probability using only a few parameters. Through experiments, we confirmed that our proposed method can acquire the prior probability efficiently and it can realize highly accurate human detection using the obtained prior probability.