DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Kihong | ko |
dc.contributor.author | Kim, Seungryong | ko |
dc.contributor.author | Sohn, Kwanghoon | ko |
dc.date.accessioned | 2024-08-16T03:00:09Z | - |
dc.date.available | 2024-08-16T03:00:09Z | - |
dc.date.created | 2024-08-16 | - |
dc.date.issued | 2018-08 | - |
dc.identifier.citation | PATTERN RECOGNITION, v.80, pp.143 - 155 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | http://hdl.handle.net/10203/322324 | - |
dc.description.abstract | Despite significant progress in machine learning, pedestrian detection in the real-world is still regarded as one of the challenging problems, limited by occluded appearances, cluttered backgrounds, and bad visibility at night. This has caused detection approaches using multi-spectral sensors such as color and thermal which could be complementary to each other. In this paper, we propose a novel sensor fusion framework for detecting pedestrians even in challenging real-world environments. We design a convolutional neural network (CNN) architecture that consists of three-branch detection models taking different modalities as inputs. Unlike existing methods, we consider all detection probabilities from each modality in a unified CNN framework and selectively use them through a channel weighting fusion (CWF) layer to maximize the detection performance. An accumulated probability fusion (APF) layer is also introduced to combine probabilities from different modalities at the proposal-level. We formulate these sub-networks into a unified network, so that it is possible to train the whole network in an end-to-end manner. Our extensive evaluation demonstrates that the proposed method outperforms the state-of-the-art methods on the challenging KAIST, CVC-14, and DIML multi-spectral pedestrian datasets. (C) 2018 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Unified multi-spectral pedestrian detection based on probabilistic fusion networks | - |
dc.type | Article | - |
dc.identifier.wosid | 000432511200012 | - |
dc.identifier.scopusid | 2-s2.0-85044133976 | - |
dc.type.rims | ART | - |
dc.citation.volume | 80 | - |
dc.citation.beginningpage | 143 | - |
dc.citation.endingpage | 155 | - |
dc.citation.publicationname | PATTERN RECOGNITION | - |
dc.identifier.doi | 10.1016/j.patcog.2018.03.007 | - |
dc.contributor.localauthor | Kim, Seungryong | - |
dc.contributor.nonIdAuthor | Park, Kihong | - |
dc.contributor.nonIdAuthor | Sohn, Kwanghoon | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Channel weighting fusion | - |
dc.subject.keywordAuthor | Probabilistic fusion | - |
dc.subject.keywordAuthor | Multi-spectral sensor fusion | - |
dc.subject.keywordAuthor | Pedestrian detection | - |
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