{"title":"基于OpenCV的ADAS道路标志识别的开发","authors":"Naina P Botekar, M. Mahalakshmi","doi":"10.1109/I2C2.2017.8321941","DOIUrl":null,"url":null,"abstract":"Real time Road sign recognition technology of advanced driver assistance systems (ADAS) provide necessary information and instructions to help the driver to drive safely. Road sign recognition is the technology of driver assistance system which interprets the signs to the driver. Recognition is dependent on the combination of detection and classification. Among the various available methods the most efficient one is chosen. Thus detection of region of interest is performed by using Histogram of oriented gradient and classification by using support vector machine. Training data is generated from our own database. This paper represents a study to recognize road signs using OpenCv techniques. This is implemented in visual studio and ported on NVIDIA's TK1 platform. The experimental results shows good performance for recognition of ideogram based signs with an average speed of 25 frames per second having accuracy up to 94%.","PeriodicalId":288351,"journal":{"name":"2017 International Conference on Intelligent Computing and Control (I2C2)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Development of road sign recognition for ADAS using OpenCV\",\"authors\":\"Naina P Botekar, M. Mahalakshmi\",\"doi\":\"10.1109/I2C2.2017.8321941\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Real time Road sign recognition technology of advanced driver assistance systems (ADAS) provide necessary information and instructions to help the driver to drive safely. Road sign recognition is the technology of driver assistance system which interprets the signs to the driver. Recognition is dependent on the combination of detection and classification. Among the various available methods the most efficient one is chosen. Thus detection of region of interest is performed by using Histogram of oriented gradient and classification by using support vector machine. Training data is generated from our own database. This paper represents a study to recognize road signs using OpenCv techniques. This is implemented in visual studio and ported on NVIDIA's TK1 platform. The experimental results shows good performance for recognition of ideogram based signs with an average speed of 25 frames per second having accuracy up to 94%.\",\"PeriodicalId\":288351,\"journal\":{\"name\":\"2017 International Conference on Intelligent Computing and Control (I2C2)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Intelligent Computing and Control (I2C2)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I2C2.2017.8321941\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Intelligent Computing and Control (I2C2)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2C2.2017.8321941","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of road sign recognition for ADAS using OpenCV
Real time Road sign recognition technology of advanced driver assistance systems (ADAS) provide necessary information and instructions to help the driver to drive safely. Road sign recognition is the technology of driver assistance system which interprets the signs to the driver. Recognition is dependent on the combination of detection and classification. Among the various available methods the most efficient one is chosen. Thus detection of region of interest is performed by using Histogram of oriented gradient and classification by using support vector machine. Training data is generated from our own database. This paper represents a study to recognize road signs using OpenCv techniques. This is implemented in visual studio and ported on NVIDIA's TK1 platform. The experimental results shows good performance for recognition of ideogram based signs with an average speed of 25 frames per second having accuracy up to 94%.
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