Vehicle detection framework for challenging lighting driving environment based on feature fusion method using adaptive neuro-fuzzy inference system
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Pae, Dong Sung | - |
dc.contributor.author | Choi, In Hwan | - |
dc.contributor.author | Kang, Tae Koo | - |
dc.contributor.author | Lim, Myo Taeg | - |
dc.date.accessioned | 2021-09-02T12:37:29Z | - |
dc.date.available | 2021-09-02T12:37:29Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-04-24 | - |
dc.identifier.issn | 1729-8814 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/76104 | - |
dc.description.abstract | This article proposes a new preceding vehicle detection framework for challenging lighting environments using a novel feature fusion technique based on an adaptive neuro-fuzzy inference system. A combination of two feature descriptors, the histogram of oriented gradients and local binary patterns, is adopted to improve the vehicle detection accuracy of the proposed framework, and the performance of the combination in image transformations is evaluated. Furthermore, we tested the detection performance of the proposed framework in three challenging driving conditions and filmed the test image sequences for each categorized environment of the experiments. The experimental results demonstrate that the proposed framework outperforms the conventional framework under specific driving environments with harsh lighting conditions. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SAGE PUBLICATIONS INC | - |
dc.subject | CLASSIFICATION | - |
dc.subject | HISTOGRAM | - |
dc.subject | MACHINE | - |
dc.subject | OBJECTS | - |
dc.title | Vehicle detection framework for challenging lighting driving environment based on feature fusion method using adaptive neuro-fuzzy inference system | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lim, Myo Taeg | - |
dc.identifier.doi | 10.1177/1729881418770545 | - |
dc.identifier.scopusid | 2-s2.0-85046902595 | - |
dc.identifier.wosid | 000431081300001 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, v.15, no.2 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS | - |
dc.citation.title | INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS | - |
dc.citation.volume | 15 | - |
dc.citation.number | 2 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Robotics | - |
dc.relation.journalWebOfScienceCategory | Robotics | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | HISTOGRAM | - |
dc.subject.keywordPlus | MACHINE | - |
dc.subject.keywordPlus | OBJECTS | - |
dc.subject.keywordAuthor | Visual object detection | - |
dc.subject.keywordAuthor | vehicle detection | - |
dc.subject.keywordAuthor | binary descriptor | - |
dc.subject.keywordAuthor | feature fusion | - |
dc.subject.keywordAuthor | adaptive neuro-fuzzy inference system | - |
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