LaRASideCam: a Fast and Robust Vision-Based Blindspot Detection System

While shifting lane on the road, the presence of a car in the blindspot can cause many accidents, since the driver does not always turn his head. Therefore, a blindspot car detection is likely to become an essential part of modern vehicles. We developed a program that detects cars in the particular...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Blanc, Nicolas, Steux, Bruno, Hinz, Thomas
Format:	Tagungsbericht
Sprache:	eng ; jpn
Schlagworte:	Image edge detection Libraries Machine learning Magnetic heads Mirrors Road accidents Robustness Support vector machines Vehicle detection Vehicles
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page	485
container_issue
container_start_page	480
container_title
container_volume
creator	Blanc, Nicolas Steux, Bruno Hinz, Thomas
description	While shifting lane on the road, the presence of a car in the blindspot can cause many accidents, since the driver does not always turn his head. Therefore, a blindspot car detection is likely to become an essential part of modern vehicles. We developed a program that detects cars in the particular configuration of blindspot using video data taken from the left or right mirror of a car, using on the one hand edge detection and support vector machine (SVM) learning and on the other hand template matching. This makes this program simple, fast and adaptative thanks to SVM learning. The program only uses basical functions of the Ecole des Mines' Camellia open-source image processing library [1], which is close to Intel's IPL library. Thus the program is easy to adapt to another API; it has already been adapted to an embedded system currently in development at NXP Semiconductors (formerly Philips Semiconductors). The source code was tested using the valgrind code checking tool [3] and was validated on real-world video sequences.
doi_str_mv	10.1109/IVS.2007.4290161
format	Conference Proceeding
fullrecord	<record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4290161</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4290161</ieee_id><sourcerecordid>4290161</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1330-50b0439cac5da33e89ea7bef0e4b45db31cdac322ee5228c3ad1d3dcfbd540e43</originalsourceid><addsrcrecordid>eNpFkEtLA0EQhMcXmETvgpf5Axu7p2f24S2JRgMBIdFcQ-9ML6xksyGzHvLvXTDgqQq-oihKqQeEMSIUT4vNemwAsrE1BWCKF2qI1liLkOZ4qQYmtSbJDNqrf5DRtRpgQZiAy7NbNYzxG8A5Y3Cg5kteTdZ1kBk3z5r1nGOneR_0qi1_erupY93ukylHCXq6q_chHtpOv0gnvuuJXp9iJ82duql4F-X-rCP1NX_9nL0ny4-3xWyyTDwSQeKgBEuFZ-8CE0leCGelVCC2tC6UhD6wJ2NE-nm5Jw4YKPiqDM72IRqpx7_eWkS2h2Pd8PG0PZ9Bv5qfTnM</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>LaRASideCam: a Fast and Robust Vision-Based Blindspot Detection System</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Blanc, Nicolas ; Steux, Bruno ; Hinz, Thomas</creator><creatorcontrib>Blanc, Nicolas ; Steux, Bruno ; Hinz, Thomas</creatorcontrib><description>While shifting lane on the road, the presence of a car in the blindspot can cause many accidents, since the driver does not always turn his head. Therefore, a blindspot car detection is likely to become an essential part of modern vehicles. We developed a program that detects cars in the particular configuration of blindspot using video data taken from the left or right mirror of a car, using on the one hand edge detection and support vector machine (SVM) learning and on the other hand template matching. This makes this program simple, fast and adaptative thanks to SVM learning. The program only uses basical functions of the Ecole des Mines' Camellia open-source image processing library [1], which is close to Intel's IPL library. Thus the program is easy to adapt to another API; it has already been adapted to an embedded system currently in development at NXP Semiconductors (formerly Philips Semiconductors). The source code was tested using the valgrind code checking tool [3] and was validated on real-world video sequences.</description><identifier>ISSN: 1931-0587</identifier><identifier>ISBN: 1424410673</identifier><identifier>ISBN: 9781424410675</identifier><identifier>EISSN: 2642-7214</identifier><identifier>EISBN: 1424410681</identifier><identifier>EISBN: 9781424410682</identifier><identifier>DOI: 10.1109/IVS.2007.4290161</identifier><language>eng ; jpn</language><publisher>IEEE</publisher><subject>Image edge detection ; Libraries ; Machine learning ; Magnetic heads ; Mirrors ; Road accidents ; Robustness ; Support vector machines ; Vehicle detection ; Vehicles</subject><ispartof>2007 IEEE Intelligent Vehicles Symposium, 2007, p.480-485</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1330-50b0439cac5da33e89ea7bef0e4b45db31cdac322ee5228c3ad1d3dcfbd540e43</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4290161$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4290161$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Blanc, Nicolas</creatorcontrib><creatorcontrib>Steux, Bruno</creatorcontrib><creatorcontrib>Hinz, Thomas</creatorcontrib><title>LaRASideCam: a Fast and Robust Vision-Based Blindspot Detection System</title><title>2007 IEEE Intelligent Vehicles Symposium</title><addtitle>IVS</addtitle><description>While shifting lane on the road, the presence of a car in the blindspot can cause many accidents, since the driver does not always turn his head. Therefore, a blindspot car detection is likely to become an essential part of modern vehicles. We developed a program that detects cars in the particular configuration of blindspot using video data taken from the left or right mirror of a car, using on the one hand edge detection and support vector machine (SVM) learning and on the other hand template matching. This makes this program simple, fast and adaptative thanks to SVM learning. The program only uses basical functions of the Ecole des Mines' Camellia open-source image processing library [1], which is close to Intel's IPL library. Thus the program is easy to adapt to another API; it has already been adapted to an embedded system currently in development at NXP Semiconductors (formerly Philips Semiconductors). The source code was tested using the valgrind code checking tool [3] and was validated on real-world video sequences.</description><subject>Image edge detection</subject><subject>Libraries</subject><subject>Machine learning</subject><subject>Magnetic heads</subject><subject>Mirrors</subject><subject>Road accidents</subject><subject>Robustness</subject><subject>Support vector machines</subject><subject>Vehicle detection</subject><subject>Vehicles</subject><issn>1931-0587</issn><issn>2642-7214</issn><isbn>1424410673</isbn><isbn>9781424410675</isbn><isbn>1424410681</isbn><isbn>9781424410682</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkEtLA0EQhMcXmETvgpf5Axu7p2f24S2JRgMBIdFcQ-9ML6xksyGzHvLvXTDgqQq-oihKqQeEMSIUT4vNemwAsrE1BWCKF2qI1liLkOZ4qQYmtSbJDNqrf5DRtRpgQZiAy7NbNYzxG8A5Y3Cg5kteTdZ1kBk3z5r1nGOneR_0qi1_erupY93ukylHCXq6q_chHtpOv0gnvuuJXp9iJ82duql4F-X-rCP1NX_9nL0ny4-3xWyyTDwSQeKgBEuFZ-8CE0leCGelVCC2tC6UhD6wJ2NE-nm5Jw4YKPiqDM72IRqpx7_eWkS2h2Pd8PG0PZ9Bv5qfTnM</recordid><startdate>200706</startdate><enddate>200706</enddate><creator>Blanc, Nicolas</creator><creator>Steux, Bruno</creator><creator>Hinz, Thomas</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200706</creationdate><title>LaRASideCam: a Fast and Robust Vision-Based Blindspot Detection System</title><author>Blanc, Nicolas ; Steux, Bruno ; Hinz, Thomas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1330-50b0439cac5da33e89ea7bef0e4b45db31cdac322ee5228c3ad1d3dcfbd540e43</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng ; jpn</language><creationdate>2007</creationdate><topic>Image edge detection</topic><topic>Libraries</topic><topic>Machine learning</topic><topic>Magnetic heads</topic><topic>Mirrors</topic><topic>Road accidents</topic><topic>Robustness</topic><topic>Support vector machines</topic><topic>Vehicle detection</topic><topic>Vehicles</topic><toplevel>online_resources</toplevel><creatorcontrib>Blanc, Nicolas</creatorcontrib><creatorcontrib>Steux, Bruno</creatorcontrib><creatorcontrib>Hinz, Thomas</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 Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Blanc, Nicolas</au><au>Steux, Bruno</au><au>Hinz, Thomas</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>LaRASideCam: a Fast and Robust Vision-Based Blindspot Detection System</atitle><btitle>2007 IEEE Intelligent Vehicles Symposium</btitle><stitle>IVS</stitle><date>2007-06</date><risdate>2007</risdate><spage>480</spage><epage>485</epage><pages>480-485</pages><issn>1931-0587</issn><eissn>2642-7214</eissn><isbn>1424410673</isbn><isbn>9781424410675</isbn><eisbn>1424410681</eisbn><eisbn>9781424410682</eisbn><abstract>While shifting lane on the road, the presence of a car in the blindspot can cause many accidents, since the driver does not always turn his head. Therefore, a blindspot car detection is likely to become an essential part of modern vehicles. We developed a program that detects cars in the particular configuration of blindspot using video data taken from the left or right mirror of a car, using on the one hand edge detection and support vector machine (SVM) learning and on the other hand template matching. This makes this program simple, fast and adaptative thanks to SVM learning. The program only uses basical functions of the Ecole des Mines' Camellia open-source image processing library [1], which is close to Intel's IPL library. Thus the program is easy to adapt to another API; it has already been adapted to an embedded system currently in development at NXP Semiconductors (formerly Philips Semiconductors). The source code was tested using the valgrind code checking tool [3] and was validated on real-world video sequences.</abstract><pub>IEEE</pub><doi>10.1109/IVS.2007.4290161</doi><tpages>6</tpages></addata></record>
fulltext	fulltext_linktorsrc
identifier	ISSN: 1931-0587
ispartof	2007 IEEE Intelligent Vehicles Symposium, 2007, p.480-485
issn	1931-0587 2642-7214
language	eng ; jpn
recordid	cdi_ieee_primary_4290161
source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Image edge detection Libraries Machine learning Magnetic heads Mirrors Road accidents Robustness Support vector machines Vehicle detection Vehicles
title	LaRASideCam: a Fast and Robust Vision-Based Blindspot Detection System
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T02%3A05%3A04IST&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=LaRASideCam:%20a%20Fast%20and%20Robust%20Vision-Based%20Blindspot%20Detection%20System&rft.btitle=2007%20IEEE%20Intelligent%20Vehicles%20Symposium&rft.au=Blanc,%20Nicolas&rft.date=2007-06&rft.spage=480&rft.epage=485&rft.pages=480-485&rft.issn=1931-0587&rft.eissn=2642-7214&rft.isbn=1424410673&rft.isbn_list=9781424410675&rft_id=info:doi/10.1109/IVS.2007.4290161&rft_dat=%3Cieee_6IE%3E4290161%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1424410681&rft.eisbn_list=9781424410682&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4290161&rfr_iscdi=true