{"title":"Block-Propagative Background Subtraction System for UHDTV Videos","authors":"A. Beaugendre, S. Goto","doi":"10.2197/ipsjtcva.7.31","DOIUrl":null,"url":null,"abstract":"The process of Ultra High Definition TV videos requires a lot of resources in terms of memory and computation time. In this paper we consider a block-propagation background subtraction (BPBGS) method which spreads to neighboring blocks if a part of an object is detected on the borders of the current block. This allows us to avoid processing unnecessary areas which do not contain any object thus saving memory and computational time. The results show that our method is particularly efficient in sequences where objects occupy a small portion of the scene despite the fact that there are a lot of background movements. At same scale our BPBGS performs much faster than the state-of-art methods for a similar detection quality.","PeriodicalId":38957,"journal":{"name":"IPSJ Transactions on Computer Vision and Applications","volume":"74 1","pages":"31-34"},"PeriodicalIF":0.0000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IPSJ Transactions on Computer Vision and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2197/ipsjtcva.7.31","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 3
Abstract
The process of Ultra High Definition TV videos requires a lot of resources in terms of memory and computation time. In this paper we consider a block-propagation background subtraction (BPBGS) method which spreads to neighboring blocks if a part of an object is detected on the borders of the current block. This allows us to avoid processing unnecessary areas which do not contain any object thus saving memory and computational time. The results show that our method is particularly efficient in sequences where objects occupy a small portion of the scene despite the fact that there are a lot of background movements. At same scale our BPBGS performs much faster than the state-of-art methods for a similar detection quality.