Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jae-Yeul | - |
dc.contributor.author | Ha, Jong-Eun | - |
dc.date.accessioned | 2021-10-17T15:00:01Z | - |
dc.date.available | 2021-10-17T15:00:01Z | - |
dc.date.created | 2021-10-07 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.citation | IEEE Access, v.9, pp.127515 - 127530 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/15582 | - |
dc.description.abstract | Proper consideration of the temporal domain and the spatial domain is essential to perform robust foreground object detection in visual surveillance. However, there are difficulties in considering long-term temporal information with CNN-based methods. To solve this limitation, classical algorithms and some deep learning-based algorithms have used a background model image. However, acquiring a sophisticated background model image is also one of the complex problems. Most of the algorithms take a lot of time to initialize the background model image and generate many errors in the presence of a static foreground. This paper proposes an algorithm for generating a background model image using a deep-learning-based segmenter to solve this problem. The proposed method shows a 66.25% lower mean square error (MSE) than the background subtraction (BGS) algorithm and 79.25% lower than the latest deep learning algorithm in the SBI dataset. In addition, in the deep learning-based segmenter that uses a background image as input, replacing the background image of BGS algorithm with the background image of the proposed method shows a 38.63% reduction in the false detection rate (PWC). © 2013 IEEE. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Generation of Background Model Image Using Foreground Model | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3111686 | - |
dc.identifier.wosid | 000697815000001 | - |
dc.identifier.scopusid | 2-s2.0-85114733412 | - |
dc.type.local | Article(Overseas) | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.citation.publicationname | IEEE Access | - |
dc.contributor.nonIdAuthor | Kim, Jae-Yeul | - |
dc.contributor.nonIdAuthor | Ha, Jong-Eun | - |
dc.identifier.citationVolume | 9 | - |
dc.identifier.citationStartPage | 127515 | - |
dc.identifier.citationEndPage | 127530 | - |
dc.identifier.citationTitle | IEEE Access | - |
dc.description.isOpenAccess | Y | - |
dc.subject.keywordAuthor | Image segmentation | - |
dc.subject.keywordAuthor | Object detection | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Surveillance | - |
dc.subject.keywordAuthor | Visualization | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Classification algorithms | - |
dc.subject.keywordAuthor | Visual surveillance | - |
dc.subject.keywordAuthor | foreground object detection | - |
dc.subject.keywordAuthor | background model image | - |
dc.subject.keywordAuthor | foreground model | - |
dc.subject.keywordPlus | SUBTRACTION | - |
dc.subject.keywordPlus | DATASET | - |
dc.subject.keywordPlus | NETWORK | - |
dc.contributor.affiliatedAuthor | Kim, Jae-Yeul | - |
dc.contributor.affiliatedAuthor | Ha, Jong-Eun | - |
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