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Domain-Adaptive Pedestrian Detection in Thermal Images
This paper presents an approach to pedestrian detection in thermal infrared (thermal) images with limited annotations. The key idea is to adapt the abundance of color images associated with bounding box annotations to the thermal domain for training the pedestrian detector. To this end, we couple a...
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creator | Guo, Tiantong Huynh, Cong Phuoc Solh, Mashhour |
description | This paper presents an approach to pedestrian detection in thermal infrared (thermal) images with limited annotations. The key idea is to adapt the abundance of color images associated with bounding box annotations to the thermal domain for training the pedestrian detector. To this end, we couple a domain adaptation component that consists of a pair of image transformers with a pedestrian detector in the thermal domain and train the entire network end-to-end. The image transformers act as a data augmentation tool that progressively improves synthetic examples on the fly for training the pedestrian detector. To aid the training process, we introduce a detection loss defined on both real thermal images and synthetic thermal images transformed from the color domain. The proposed detector outperforms existing methods on the thermal images from the KAIST detection benchmark [1]. |
doi_str_mv | 10.1109/ICIP.2019.8803104 |
format | conference_proceeding |
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The key idea is to adapt the abundance of color images associated with bounding box annotations to the thermal domain for training the pedestrian detector. To this end, we couple a domain adaptation component that consists of a pair of image transformers with a pedestrian detector in the thermal domain and train the entire network end-to-end. The image transformers act as a data augmentation tool that progressively improves synthetic examples on the fly for training the pedestrian detector. To aid the training process, we introduce a detection loss defined on both real thermal images and synthetic thermal images transformed from the color domain. 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The key idea is to adapt the abundance of color images associated with bounding box annotations to the thermal domain for training the pedestrian detector. To this end, we couple a domain adaptation component that consists of a pair of image transformers with a pedestrian detector in the thermal domain and train the entire network end-to-end. The image transformers act as a data augmentation tool that progressively improves synthetic examples on the fly for training the pedestrian detector. To aid the training process, we introduce a detection loss defined on both real thermal images and synthetic thermal images transformed from the color domain. The proposed detector outperforms existing methods on the thermal images from the KAIST detection benchmark [1].</description><subject>Color</subject><subject>deep learning</subject><subject>Detectors</subject><subject>Generators</subject><subject>Image color analysis</subject><subject>Lighting</subject><subject>pedestrian detection</subject><subject>synthetic image</subject><subject>thermal image</subject><subject>Training</subject><subject>Two dimensional displays</subject><issn>2381-8549</issn><isbn>9781538662496</isbn><isbn>1538662493</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2019</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj81Kw0AUhUdBsLZ9AHEzL5A4d25mcu-ypP4ECnbRrss4uepIk5YkCL69Abs6Z_HxcY5S92ByAMOPdVVvc2uAcyKDYIorteSSwCF5bwv212pmkSAjV_CtuhuGb2MmHmGm_PrUhtRlqyacx_QjeiuNDGOfQqfXMkoc06nTqdO7L-nbcNR1Gz5lWKibj3AcZHnJudo_P-2q12zz9lJXq02WrMExi5EwiocSLOJ74TyJZyY3dUKGUjy6iXA2RCsMjRCXYZoW0bNQWeBcPfx7k4gczn1qQ_97uNzEPwOqRCU</recordid><startdate>201909</startdate><enddate>201909</enddate><creator>Guo, Tiantong</creator><creator>Huynh, Cong Phuoc</creator><creator>Solh, Mashhour</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201909</creationdate><title>Domain-Adaptive Pedestrian Detection in Thermal Images</title><author>Guo, Tiantong ; Huynh, Cong Phuoc ; Solh, Mashhour</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-cc83ce6171233b4568e699853b483917e63583c52ac2e91de897a020c369e8743</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Color</topic><topic>deep learning</topic><topic>Detectors</topic><topic>Generators</topic><topic>Image color analysis</topic><topic>Lighting</topic><topic>pedestrian detection</topic><topic>synthetic image</topic><topic>thermal image</topic><topic>Training</topic><topic>Two dimensional displays</topic><toplevel>online_resources</toplevel><creatorcontrib>Guo, Tiantong</creatorcontrib><creatorcontrib>Huynh, Cong Phuoc</creatorcontrib><creatorcontrib>Solh, Mashhour</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore (Online service)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Guo, Tiantong</au><au>Huynh, Cong Phuoc</au><au>Solh, Mashhour</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Domain-Adaptive Pedestrian Detection in Thermal Images</atitle><btitle>2019 IEEE International Conference on Image Processing (ICIP)</btitle><stitle>ICIP</stitle><date>2019-09</date><risdate>2019</risdate><spage>1660</spage><epage>1664</epage><pages>1660-1664</pages><eissn>2381-8549</eissn><eisbn>9781538662496</eisbn><eisbn>1538662493</eisbn><abstract>This paper presents an approach to pedestrian detection in thermal infrared (thermal) images with limited annotations. The key idea is to adapt the abundance of color images associated with bounding box annotations to the thermal domain for training the pedestrian detector. To this end, we couple a domain adaptation component that consists of a pair of image transformers with a pedestrian detector in the thermal domain and train the entire network end-to-end. The image transformers act as a data augmentation tool that progressively improves synthetic examples on the fly for training the pedestrian detector. To aid the training process, we introduce a detection loss defined on both real thermal images and synthetic thermal images transformed from the color domain. The proposed detector outperforms existing methods on the thermal images from the KAIST detection benchmark [1].</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2019.8803104</doi><tpages>5</tpages></addata></record> |
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identifier | EISSN: 2381-8549 |
ispartof | 2019 IEEE International Conference on Image Processing (ICIP), 2019, p.1660-1664 |
issn | 2381-8549 |
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source | IEEE Xplore All Conference Series |
subjects | Color deep learning Detectors Generators Image color analysis Lighting pedestrian detection synthetic image thermal image Training Two dimensional displays |
title | Domain-Adaptive Pedestrian Detection in Thermal Images |
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