Identifying Reflected Images From Object Detector in Indoor Environment Utilizing Depth Information

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dc.contributor.authorPark, Daeheeko
dc.contributor.authorPark, Yong-Hwako
dc.date.accessioned2021-02-05T05:30:06Z-
dc.date.available2021-02-05T05:30:06Z-
dc.date.created2021-02-05-
dc.date.created2021-02-05-
dc.date.created2021-02-05-
dc.date.issued2021-04-
dc.identifier.citationIEEE ROBOTICS AND AUTOMATION LETTERS, v.6, no.2, pp.635 - 642-
dc.identifier.issn2377-3766-
dc.identifier.urihttp://hdl.handle.net/10203/280599-
dc.description.abstractWe observed that mirror reflection severely de- grades person detection performance in an indoor environment, which is an essential task for service robots. To address this problem, we propose a new real-time method to identify reflected virtual images in an indoor environment utilizing 3D depth information. Images reflected by the mirror are similar to real objects, so it is a non-trivial task to differentiate them. Conventional object detectors, which do not deal with this problem, obviously recognize reflected images as real objects. The proposed method compares the geometric relationship between the 3D spatial information of the detected object and its surrounding environment where the object locates. It analyzes the layout of surrounding indoor space utilizing semantic segmentation and plane detection method. With the estimated layout of indoor space, detected object candidates are examined whether they are real or reflected images utilizing 3D depth information. To verify the proposed method, a large indoor dataset was newly acquired and examined in a dedicated Living-lab environment. The performance of the algorithm is verified by comparing conventional detectors with the proposed method in the acquired Living-lab dataset.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleIdentifying Reflected Images From Object Detector in Indoor Environment Utilizing Depth Information-
dc.typeArticle-
dc.identifier.wosid000612129400002-
dc.identifier.scopusid2-s2.0-85099104107-
dc.type.rimsART-
dc.citation.volume6-
dc.citation.issue2-
dc.citation.beginningpage635-
dc.citation.endingpage642-
dc.citation.publicationnameIEEE ROBOTICS AND AUTOMATION LETTERS-
dc.identifier.doi10.1109/lra.2020.3047796-
dc.contributor.localauthorPark, Yong-Hwa-
dc.contributor.nonIdAuthorPark, Daehee-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorMirrors-
dc.subject.keywordAuthorIndoor environment-
dc.subject.keywordAuthorThree-dimensional displays-
dc.subject.keywordAuthorCameras-
dc.subject.keywordAuthorLayout-
dc.subject.keywordAuthorImage segmentation-
dc.subject.keywordAuthorDetectors-
dc.subject.keywordAuthorDeep learning for visual perception-
dc.subject.keywordAuthorhuman detection and tracking-
dc.subject.keywordAuthorRGB-D perception-
dc.subject.keywordAuthorservice robotics-

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