Loading…
Fast Pedestrian Detection Using a Night Vision System for Safety Driving
This paper proposes a rapid pedestrian-detection algorithm for thermal images by using energy symmetry and oriented center-symmetric local binary pattern (OCS-LBP) features based on luminance saliency. During preprocessing, energy symmetry based on luminance saliency is used as the filter to remove...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Conference Proceeding |
| Language: | English |
| Subjects: | |
| Online Access: | Request full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | |
| container_end_page | 72 |
| container_issue | |
| container_start_page | 69 |
| container_title | |
| container_volume | |
| creator | Mi Ra Jeong Jun-Yong Kwak Jung Eun Son ByoungChul Ko Jae-Yeal Nam |
| description | This paper proposes a rapid pedestrian-detection algorithm for thermal images by using energy symmetry and oriented center-symmetric local binary pattern (OCS-LBP) features based on luminance saliency. During preprocessing, energy symmetry based on luminance saliency is used as the filter to remove objects and to reduce the pedestrian classification time. The OCS-LBP feature is then extracted from a candidate window and subjected to a random forest classifier (RF) that separates candidate windows into pedestrian and non-pedestrian classes. The proposed algorithm has been successfully applied to various thermal images captured in a car, and its detection performance is better than that of other methods. |
| doi_str_mv | 10.1109/CGiV.2014.25 |
| format | conference_proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6934123</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6934123</ieee_id><sourcerecordid>6934123</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-721cfa0afed98cf4f7829fa77e6ea1e9ed3105e52d984381da5d160405f6f423</originalsourceid><addsrcrecordid>eNotjE1LAzEUReNCUGt37tzkD8z4Xj4myVKmthWKLbR2W8LMS43YqUyCMP_ekbq6cM89l7EHhBIR3FO9iPtSAKpS6Ct2h8o4p40Ae8OmKX0CALrKglW3bDn3KfMNtZRyH33HZ5SpyfHc8fcUuyP3_C0ePzLfx_RXboeU6cTDuedbHygPfNbHn3F4z66D_0o0_c8J281fdvWyWK0Xr_XzqohodC6MwCZ4GNXW2SaoYKxwwRtDFXkkR61E0KTFiJW02HrdYgUKdKiCEnLCHi-3kYgO3308-X44VE4qFFL-AigLSSE</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Fast Pedestrian Detection Using a Night Vision System for Safety Driving</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Mi Ra Jeong ; Jun-Yong Kwak ; Jung Eun Son ; ByoungChul Ko ; Jae-Yeal Nam</creator><creatorcontrib>Mi Ra Jeong ; Jun-Yong Kwak ; Jung Eun Son ; ByoungChul Ko ; Jae-Yeal Nam</creatorcontrib><description>This paper proposes a rapid pedestrian-detection algorithm for thermal images by using energy symmetry and oriented center-symmetric local binary pattern (OCS-LBP) features based on luminance saliency. During preprocessing, energy symmetry based on luminance saliency is used as the filter to remove objects and to reduce the pedestrian classification time. The OCS-LBP feature is then extracted from a candidate window and subjected to a random forest classifier (RF) that separates candidate windows into pedestrian and non-pedestrian classes. The proposed algorithm has been successfully applied to various thermal images captured in a car, and its detection performance is better than that of other methods.</description><identifier>EISBN: 1479957208</identifier><identifier>EISBN: 9781479957200</identifier><identifier>DOI: 10.1109/CGiV.2014.25</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>Cameras ; Classification algorithms ; Feature extraction ; Pedestrian detection; thermal image; luminance saliency; energy symmetry; random forest ; Radio frequency ; Thermal energy ; Thermal sensors ; Training</subject><ispartof>2014 11th International Conference on Computer Graphics, Imaging and Visualization, 2014, p.69-72</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6934123$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6934123$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Mi Ra Jeong</creatorcontrib><creatorcontrib>Jun-Yong Kwak</creatorcontrib><creatorcontrib>Jung Eun Son</creatorcontrib><creatorcontrib>ByoungChul Ko</creatorcontrib><creatorcontrib>Jae-Yeal Nam</creatorcontrib><title>Fast Pedestrian Detection Using a Night Vision System for Safety Driving</title><title>2014 11th International Conference on Computer Graphics, Imaging and Visualization</title><addtitle>CGIV</addtitle><description>This paper proposes a rapid pedestrian-detection algorithm for thermal images by using energy symmetry and oriented center-symmetric local binary pattern (OCS-LBP) features based on luminance saliency. During preprocessing, energy symmetry based on luminance saliency is used as the filter to remove objects and to reduce the pedestrian classification time. The OCS-LBP feature is then extracted from a candidate window and subjected to a random forest classifier (RF) that separates candidate windows into pedestrian and non-pedestrian classes. The proposed algorithm has been successfully applied to various thermal images captured in a car, and its detection performance is better than that of other methods.</description><subject>Cameras</subject><subject>Classification algorithms</subject><subject>Feature extraction</subject><subject>Pedestrian detection; thermal image; luminance saliency; energy symmetry; random forest</subject><subject>Radio frequency</subject><subject>Thermal energy</subject><subject>Thermal sensors</subject><subject>Training</subject><isbn>1479957208</isbn><isbn>9781479957200</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2014</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjE1LAzEUReNCUGt37tzkD8z4Xj4myVKmthWKLbR2W8LMS43YqUyCMP_ekbq6cM89l7EHhBIR3FO9iPtSAKpS6Ct2h8o4p40Ae8OmKX0CALrKglW3bDn3KfMNtZRyH33HZ5SpyfHc8fcUuyP3_C0ePzLfx_RXboeU6cTDuedbHygPfNbHn3F4z66D_0o0_c8J281fdvWyWK0Xr_XzqohodC6MwCZ4GNXW2SaoYKxwwRtDFXkkR61E0KTFiJW02HrdYgUKdKiCEnLCHi-3kYgO3308-X44VE4qFFL-AigLSSE</recordid><startdate>201408</startdate><enddate>201408</enddate><creator>Mi Ra Jeong</creator><creator>Jun-Yong Kwak</creator><creator>Jung Eun Son</creator><creator>ByoungChul Ko</creator><creator>Jae-Yeal Nam</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201408</creationdate><title>Fast Pedestrian Detection Using a Night Vision System for Safety Driving</title><author>Mi Ra Jeong ; Jun-Yong Kwak ; Jung Eun Son ; ByoungChul Ko ; Jae-Yeal Nam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-721cfa0afed98cf4f7829fa77e6ea1e9ed3105e52d984381da5d160405f6f423</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Cameras</topic><topic>Classification algorithms</topic><topic>Feature extraction</topic><topic>Pedestrian detection; thermal image; luminance saliency; energy symmetry; random forest</topic><topic>Radio frequency</topic><topic>Thermal energy</topic><topic>Thermal sensors</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Mi Ra Jeong</creatorcontrib><creatorcontrib>Jun-Yong Kwak</creatorcontrib><creatorcontrib>Jung Eun Son</creatorcontrib><creatorcontrib>ByoungChul Ko</creatorcontrib><creatorcontrib>Jae-Yeal Nam</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mi Ra Jeong</au><au>Jun-Yong Kwak</au><au>Jung Eun Son</au><au>ByoungChul Ko</au><au>Jae-Yeal Nam</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Fast Pedestrian Detection Using a Night Vision System for Safety Driving</atitle><btitle>2014 11th International Conference on Computer Graphics, Imaging and Visualization</btitle><stitle>CGIV</stitle><date>2014-08</date><risdate>2014</risdate><spage>69</spage><epage>72</epage><pages>69-72</pages><eisbn>1479957208</eisbn><eisbn>9781479957200</eisbn><coden>IEEPAD</coden><abstract>This paper proposes a rapid pedestrian-detection algorithm for thermal images by using energy symmetry and oriented center-symmetric local binary pattern (OCS-LBP) features based on luminance saliency. During preprocessing, energy symmetry based on luminance saliency is used as the filter to remove objects and to reduce the pedestrian classification time. The OCS-LBP feature is then extracted from a candidate window and subjected to a random forest classifier (RF) that separates candidate windows into pedestrian and non-pedestrian classes. The proposed algorithm has been successfully applied to various thermal images captured in a car, and its detection performance is better than that of other methods.</abstract><pub>IEEE</pub><doi>10.1109/CGiV.2014.25</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISBN: 1479957208 |
| ispartof | 2014 11th International Conference on Computer Graphics, Imaging and Visualization, 2014, p.69-72 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_6934123 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Cameras Classification algorithms Feature extraction Pedestrian detection thermal image luminance saliency energy symmetry random forest Radio frequency Thermal energy Thermal sensors Training |
| title | Fast Pedestrian Detection Using a Night Vision System for Safety Driving |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-06T15%3A43%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Fast%20Pedestrian%20Detection%20Using%20a%20Night%20Vision%20System%20for%20Safety%20Driving&rft.btitle=2014%2011th%20International%20Conference%20on%20Computer%20Graphics,%20Imaging%20and%20Visualization&rft.au=Mi%20Ra%20Jeong&rft.date=2014-08&rft.spage=69&rft.epage=72&rft.pages=69-72&rft.coden=IEEPAD&rft_id=info:doi/10.1109/CGiV.2014.25&rft.eisbn=1479957208&rft.eisbn_list=9781479957200&rft_dat=%3Cieee_6IE%3E6934123%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-721cfa0afed98cf4f7829fa77e6ea1e9ed3105e52d984381da5d160405f6f423%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6934123&rfr_iscdi=true |