Integrating Appearance and Edge Features for Sedan Vehicle Detection in the Blind-Spot Area
Changing lanes while having no information about the blind spot area can be dangerous. We propose a vision-based vehicle detection system for a lane changing assistance system to monitor the potential sedan vehicle in the blind-spot area. To serve our purpose, we select adequate features, which are...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2012-06, Vol.13 (2), p.737-747 |
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| container_title | IEEE transactions on intelligent transportation systems |
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| creator | Bin-Feng Lin Yi-Ming Chan Fu, Li-Chen Pei-Yung Hsiao Li-An Chuang Shin-Shinh Huang Min-Fang Lo |
| description | Changing lanes while having no information about the blind spot area can be dangerous. We propose a vision-based vehicle detection system for a lane changing assistance system to monitor the potential sedan vehicle in the blind-spot area. To serve our purpose, we select adequate features, which are directly obtained from vehicle images, to detect possible vehicles in the blind-spot area. This is challenging due to the significant change in the view angle of a vehicle along with its location throughout the blind-spot area. To cope with this problem, we propose a method to combine two kinds of part-based features that are related to the characteristics of the vehicle, and we build multiple models based on different viewpoints of a vehicle. The location information of each feature is incorporated to help construct the detector and estimate the reasonable position of the presence of the vehicle. The experiments show that our system is reliable in detecting various sedan vehicles in the blind-spot area. |
| doi_str_mv | 10.1109/TITS.2011.2182649 |
| format | Article |
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We propose a vision-based vehicle detection system for a lane changing assistance system to monitor the potential sedan vehicle in the blind-spot area. To serve our purpose, we select adequate features, which are directly obtained from vehicle images, to detect possible vehicles in the blind-spot area. This is challenging due to the significant change in the view angle of a vehicle along with its location throughout the blind-spot area. To cope with this problem, we propose a method to combine two kinds of part-based features that are related to the characteristics of the vehicle, and we build multiple models based on different viewpoints of a vehicle. The location information of each feature is incorporated to help construct the detector and estimate the reasonable position of the presence of the vehicle. The experiments show that our system is reliable in detecting various sedan vehicles in the blind-spot area.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2011.2182649</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Blind spot area ; Construction ; Dangerous ; Estimates ; Feature extraction ; feature integration ; Image edge detection ; Image segmentation ; Monitors ; Position (location) ; spatial relationship ; Training ; Vectors ; Vehicle detection ; Vehicles</subject><ispartof>IEEE transactions on intelligent transportation systems, 2012-06, Vol.13 (2), p.737-747</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jun 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c435t-8ad309f1b5db2cece572e30bfe14ad9c19269acea8957ddc0d74806561b328043</citedby><cites>FETCH-LOGICAL-c435t-8ad309f1b5db2cece572e30bfe14ad9c19269acea8957ddc0d74806561b328043</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6145682$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54737</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6145682$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Bin-Feng Lin</creatorcontrib><creatorcontrib>Yi-Ming Chan</creatorcontrib><creatorcontrib>Fu, Li-Chen</creatorcontrib><creatorcontrib>Pei-Yung Hsiao</creatorcontrib><creatorcontrib>Li-An Chuang</creatorcontrib><creatorcontrib>Shin-Shinh Huang</creatorcontrib><creatorcontrib>Min-Fang Lo</creatorcontrib><title>Integrating Appearance and Edge Features for Sedan Vehicle Detection in the Blind-Spot Area</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>Changing lanes while having no information about the blind spot area can be dangerous. We propose a vision-based vehicle detection system for a lane changing assistance system to monitor the potential sedan vehicle in the blind-spot area. To serve our purpose, we select adequate features, which are directly obtained from vehicle images, to detect possible vehicles in the blind-spot area. This is challenging due to the significant change in the view angle of a vehicle along with its location throughout the blind-spot area. To cope with this problem, we propose a method to combine two kinds of part-based features that are related to the characteristics of the vehicle, and we build multiple models based on different viewpoints of a vehicle. The location information of each feature is incorporated to help construct the detector and estimate the reasonable position of the presence of the vehicle. The experiments show that our system is reliable in detecting various sedan vehicles in the blind-spot area.</description><subject>Blind spot area</subject><subject>Construction</subject><subject>Dangerous</subject><subject>Estimates</subject><subject>Feature extraction</subject><subject>feature integration</subject><subject>Image edge detection</subject><subject>Image segmentation</subject><subject>Monitors</subject><subject>Position (location)</subject><subject>spatial relationship</subject><subject>Training</subject><subject>Vectors</subject><subject>Vehicle detection</subject><subject>Vehicles</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkMFKAzEQhhdRsFYfQLwEvHjZmskmafZYtdVCwUOrFw9Lmsy2W7bZNckefHu3tHgQBmYYvn8YviS5BToCoPnjar5ajhgFGDFQTPL8LBmAECqlFOT5YWY8zamgl8lVCLt-ywXAIPmau4gbr2PlNmTStqi9dgaJdpZM7QbJDHXsPAZSNp4s0WpHPnFbmRrJC0Y0sWocqRyJWyRPdeVsumybSCYe9XVyUeo64M2pD5OP2XT1_JYu3l_nz5NFangmYqq0zWhewlrYNTNoUIwZZnRdInBtcwM5k7k2qFUuxtYaasdcUSkkrDOmKM-GycPxbuub7w5DLPZVMFjX2mHThQIyKYCzvnr0_h-6azrv-u8KoKC45IypnoIjZXwTgseyaH211_6nh4qD7uKguzjoLk66-8zdMVMh4h8ve81SsewXnnt6Xw</recordid><startdate>20120601</startdate><enddate>20120601</enddate><creator>Bin-Feng Lin</creator><creator>Yi-Ming Chan</creator><creator>Fu, Li-Chen</creator><creator>Pei-Yung Hsiao</creator><creator>Li-An Chuang</creator><creator>Shin-Shinh Huang</creator><creator>Min-Fang Lo</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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We propose a vision-based vehicle detection system for a lane changing assistance system to monitor the potential sedan vehicle in the blind-spot area. To serve our purpose, we select adequate features, which are directly obtained from vehicle images, to detect possible vehicles in the blind-spot area. This is challenging due to the significant change in the view angle of a vehicle along with its location throughout the blind-spot area. To cope with this problem, we propose a method to combine two kinds of part-based features that are related to the characteristics of the vehicle, and we build multiple models based on different viewpoints of a vehicle. The location information of each feature is incorporated to help construct the detector and estimate the reasonable position of the presence of the vehicle. 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| source | IEEE Electronic Library (IEL) |
| subjects | Blind spot area Construction Dangerous Estimates Feature extraction feature integration Image edge detection Image segmentation Monitors Position (location) spatial relationship Training Vectors Vehicle detection Vehicles |
| title | Integrating Appearance and Edge Features for Sedan Vehicle Detection in the Blind-Spot Area |
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