{"title":"Learning local histogram representation for efficient traffic sign recognition","authors":"Jinlu Gao, Yuqiang Fang, Xingwei Li","doi":"10.1109/CISP.2015.7407955","DOIUrl":null,"url":null,"abstract":"With the rising of intelligent vehicle technologies, traffic sign recognition become an essential problem in computer vision. Focusing on the traffic sign recognition under real-world scenario, this paper aims to develop novel local feature representation to improve the traffic sign recognition performance. Especially, with the local histogram feature as a basic unit, a novel histogram intersection kernel based dictionary learning method is proposed for feature quantization. Then a fast feature encoding approach based on look-up table is induced to improve the calculation effectiveness. The proposed recognition method achieves high performance on several off-line traffic sign databases, and has also been extended to recognize traffic sign in real-world videos. Extensive experiments have demonstrated the effectiveness of new method.","PeriodicalId":167631,"journal":{"name":"2015 8th International Congress on Image and Signal Processing (CISP)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 8th International Congress on Image and Signal Processing (CISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISP.2015.7407955","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
With the rising of intelligent vehicle technologies, traffic sign recognition become an essential problem in computer vision. Focusing on the traffic sign recognition under real-world scenario, this paper aims to develop novel local feature representation to improve the traffic sign recognition performance. Especially, with the local histogram feature as a basic unit, a novel histogram intersection kernel based dictionary learning method is proposed for feature quantization. Then a fast feature encoding approach based on look-up table is induced to improve the calculation effectiveness. The proposed recognition method achieves high performance on several off-line traffic sign databases, and has also been extended to recognize traffic sign in real-world videos. Extensive experiments have demonstrated the effectiveness of new method.