{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T22:45:13Z","timestamp":1726440313478},"reference-count":17,"publisher":"Wiley","license":[{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2013]]},"abstract":"Vehicle logo detection from images captured by surveillance cameras is an important step towards the vehicle recognition that is required for many applications in intelligent transportation systems and automatic surveillance. The task is challenging considering the small target of logos and the wide range of variability in shape, color, and illumination. A fast and reliable vehicle logo detection approach is proposed following visual attention mechanism from the human vision. Two prelogo detection steps, that is, vehicle region detection and a small RoI segmentation, rapidly focalize a small logo target. An enhanced Adaboost algorithm, together with two types of features of Haar and HOG, is proposed to detect vehicles. An RoI that covers logos is segmented based on our prior knowledge about the logos\u2019 position relative to license plates, which can be accurately localized from frontal vehicle images. A two-stage cascade classier proceeds with the segmented RoI, using a hybrid of Gentle Adaboost and Support Vector Machine (SVM), resulting in precise logo positioning. Extensive experiments were conducted to verify the efficiency of the proposed scheme.<\/jats:p>","DOI":"10.1155\/2013\/391652","type":"journal-article","created":{"date-parts":[[2013,10,27]],"date-time":"2013-10-27T17:02:50Z","timestamp":1382893370000},"page":"1-12","source":"Crossref","is-referenced-by-count":7,"title":["An Integrative Approach to Accurate Vehicle Logo Detection"],"prefix":"10.1155","volume":"2013","author":[{"given":"Hao","family":"Pan","sequence":"first","affiliation":[{"name":"Lucas Varity Langzhong Brake Co., Ltd, Langfang Development Zone, Hebei 065001, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5472-2100","authenticated-orcid":true,"given":"Bailing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Xi\u2019an Jiaotong-Liverpool University, SIP, Suzhou 215123, China"}]}],"member":"311","reference":[{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2040177"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1504\/IJCVR.2012.046414"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2012.2213814"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2013.2245725"},{"key":"7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/11760191_10","volume-title":"Neural network approach to identify model of vehicles","volume":"3973","year":"2006"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2042714"},{"first-page":"1311","volume-title":"Fast and robust classification using asymmetric AdaBoost and a detector cascade","year":"2001","key":"11"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2007.894194"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1155\/2008\/782432"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1016\/0001-6918(93)90042-P"},{"first-page":"521","volume-title":"Computational models of visual attention","year":"2002","key":"17"},{"first-page":"169","volume-title":"The level of attention attention: mediating between the stimulus and perception","year":"2003","key":"18"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2004.09.004"},{"year":"2008","key":"23"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"issue":"2","key":"25","first-page":"337","volume":"28","year":"2000","journal-title":"The Annals of Statistics"},{"key":"29","first-page":"273","volume":"20","year":"1995","journal-title":"Machine Learning"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2013\/391652.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2013\/391652.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2013\/391652.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,21]],"date-time":"2017-06-21T20:53:02Z","timestamp":1498078382000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.hindawi.com\/journals\/jece\/2013\/391652\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013]]},"references-count":17,"alternative-id":["391652","391652"],"URL":"https:\/\/doi.org\/10.1155\/2013\/391652","relation":{},"ISSN":["2090-0147","2090-0155"],"issn-type":[{"type":"print","value":"2090-0147"},{"type":"electronic","value":"2090-0155"}],"subject":[],"published":{"date-parts":[[2013]]}}}