DC Field | Value | Language |
---|---|---|
dc.contributor.author | Han, B | - |
dc.contributor.author | Larry S. Davis | - |
dc.date.accessioned | 2016-03-31T09:02:38Z | - |
dc.date.available | 2016-03-31T09:02:38Z | - |
dc.date.created | 2012-04-05 | - |
dc.date.issued | 2012-05 | - |
dc.identifier.issn | 0162-8828 | - |
dc.identifier.other | 2012-OAK-0000025406 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/16530 | - |
dc.description.abstract | Background modeling and subtraction is a natural technique for object detection in videos captured by a static camera, and also a critical preprocessing step in various high-level computer vision applications. However, there have not been many studies concerning useful features and binary segmentation algorithms for this problem. We propose a pixelwise background modeling and subtraction technique using multiple features, where generative and discriminative techniques are combined for classification. In our algorithm, color, gradient, and Haar-like features are integrated to handle spatio-temporal variations for each pixel. A pixelwise generative background model is obtained for each feature efficiently and effectively by Kernel Density Approximation (KDA). Background subtraction is performed in a discriminative manner using a Support Vector Machine (SVM) over background likelihood vectors for a set of features. The proposed algorithm is robust to shadow, illumination changes, spatial variations of background. We compare the performance of the algorithm with other density-based methods using several different feature combinations and modeling techniques, both quantitatively and qualitatively. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | IEEE | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | - |
dc.subject | Background modeling and subtraction | - |
dc.subject | Haar-like features | - |
dc.subject | support vector machine | - |
dc.subject | kernel density approximation | - |
dc.subject | REAL-TIME TRACKING | - |
dc.subject | OBJECT DETECTION | - |
dc.title | Density-Based Multifeature Background Subtraction with Support Vector Machine | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1109/TPAMI.2011.243 | - |
dc.author.google | Han, B | - |
dc.author.google | Davis, LS | - |
dc.relation.volume | 34 | - |
dc.relation.issue | 5 | - |
dc.relation.startpage | 1017 | - |
dc.relation.lastpage | 1023 | - |
dc.contributor.id | 10652580 | - |
dc.relation.journal | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.34, no.5, pp.1017 - 1023 | - |
dc.identifier.wosid | 000301747400014 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 1023 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 1017 | - |
dc.citation.title | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | - |
dc.citation.volume | 34 | - |
dc.contributor.affiliatedAuthor | Han, B | - |
dc.identifier.scopusid | 2-s2.0-84859175807 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 62 | - |
dc.description.scptc | 68 | * |
dc.date.scptcdate | 2018-05-121 | * |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Background modeling and subtraction | - |
dc.subject.keywordAuthor | Haar-like features | - |
dc.subject.keywordAuthor | support vector machine | - |
dc.subject.keywordAuthor | kernel density approximation | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
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