Mohak Sukhwani, Suriya Singh, Anirudh Goyal, Aseem Behl, Pritish Mohapatra, B. Bharti, C. V. Jawahar
{"title":"Monocular vision based road marking recognition for driver assistance and safety","authors":"Mohak Sukhwani, Suriya Singh, Anirudh Goyal, Aseem Behl, Pritish Mohapatra, B. Bharti, C. V. Jawahar","doi":"10.1109/ICVES.2014.7063716","DOIUrl":null,"url":null,"abstract":"In this paper, we present a solution to generate semantically richer descriptions and instructions for driver assistance and safety. Our solution builds upon a set of computer vision and machine learning modules. We start with low-level image processing and finally generate high-level descriptions. We do this by combining the results of the image pattern recognition module with the prior knowledge on traffic rules and larger context present in the video sequence. For recognition of road markings, we use a SVM based classifier and HOG based classifier. We test our method on real data captured in urban settings, and report impressive performance. Qualitative and quantitative performance of various modules are presented.","PeriodicalId":248904,"journal":{"name":"2014 IEEE International Conference on Vehicular Electronics and Safety","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Vehicular Electronics and Safety","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICVES.2014.7063716","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper, we present a solution to generate semantically richer descriptions and instructions for driver assistance and safety. Our solution builds upon a set of computer vision and machine learning modules. We start with low-level image processing and finally generate high-level descriptions. We do this by combining the results of the image pattern recognition module with the prior knowledge on traffic rules and larger context present in the video sequence. For recognition of road markings, we use a SVM based classifier and HOG based classifier. We test our method on real data captured in urban settings, and report impressive performance. Qualitative and quantitative performance of various modules are presented.
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