A neuro-fuzzy pedestrian detection method using convolutional multiblock HOG
DC Field | Value | Language |
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dc.contributor.author | Myung, Kun-Woo | - |
dc.contributor.author | Qu, Le-Tao | - |
dc.contributor.author | Lim, Joon-Shik | - |
dc.date.available | 2020-02-27T20:42:30Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2017-07 | - |
dc.identifier.issn | 1975-8359 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/6667 | - |
dc.description.abstract | Pedestrian detection is a very important and valuable part of artificial intelligence and computer vision. It can be used in various areas for example automatic drive, video analysis and others. Many works have been done for the pedestrian detection. The accuracy of pedestrian detection on multiple pedestrian image has reached high level. It is not easily get more progress now. This paper proposes a new structure based on the idea of HOG and convolutional filters to do the pedestrian detection in single pedestrian image. It can be a method to increase the accuracy depend on the high accuracy in single pedestrian detection. In this paper, we use Multiblock HOG and magnitude of the pixel as the feature and use convolutional filter to do the to extract the feature. And then use NEWFM to be the classifier for training and testing. We use single pedestrian image of the INRIA data set as the data set. The result shows that the Convolutional Multiblock HOG we proposed get better performance which is 0.015 miss rate at 10-4 false positive than the other detection methods for example HOGLBP which is 0.03 miss rate and ChnFtrs which is 0.075 miss rate. Copyright © The Korean Institute of Electrical Engineers. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Korean Institute of Electrical Engineers | - |
dc.relation.isPartOf | Transactions of the Korean Institute of Electrical Engineers | - |
dc.title | A neuro-fuzzy pedestrian detection method using convolutional multiblock HOG | - |
dc.title.alternative | 컨볼루션 멀티블럭 HOG를 이용한 퍼지신경망 보행자 검출 방법 | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.5370/KIEE.2017.66.7.1117 | - |
dc.identifier.bibliographicCitation | Transactions of the Korean Institute of Electrical Engineers, v.66, no.7, pp.1117 - 1122 | - |
dc.identifier.kciid | ART002242549 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85030567270 | - |
dc.citation.endPage | 1122 | - |
dc.citation.startPage | 1117 | - |
dc.citation.title | Transactions of the Korean Institute of Electrical Engineers | - |
dc.citation.volume | 66 | - |
dc.citation.number | 7 | - |
dc.contributor.affiliatedAuthor | Myung, Kun-Woo | - |
dc.contributor.affiliatedAuthor | Qu, Le-Tao | - |
dc.contributor.affiliatedAuthor | Lim, Joon-Shik | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | INRIA data set | - |
dc.subject.keywordAuthor | Multiblock HOG | - |
dc.subject.keywordAuthor | NEWFM | - |
dc.subject.keywordAuthor | Pedestrian detection | - |
dc.subject.keywordPlus | Digital storage | - |
dc.subject.keywordPlus | Data set | - |
dc.subject.keywordPlus | Detection methods | - |
dc.subject.keywordPlus | False positive | - |
dc.subject.keywordPlus | Multi blocks | - |
dc.subject.keywordPlus | NEWFM | - |
dc.subject.keywordPlus | Pedestrian detection | - |
dc.subject.keywordPlus | Structure-based | - |
dc.subject.keywordPlus | Training and testing | - |
dc.subject.keywordPlus | Convolution | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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