{"title":"驾驶员辅助系统的增强运动估计。弯曲道路模型的集成","authors":"Gregor Schewior, H. Blume","doi":"10.1109/GCCE.2013.6664897","DOIUrl":null,"url":null,"abstract":"This paper presents an extended efficient modelbased approach for the improvement of motion vector fields for driver assistance systems. Through the integration of a curved road model the quality of motion vector fields, estimated by a predictive block-based motion estimator working on video signals which are captured by a front camera inside a car, is significantly increased. The optimization step is performed by modeling synthetic motion vector fields of typical driving scenes acting in an iterative step as additional motion vector candidates. The proposed enhanced approach which is effective for all kinds of driving situations provides a significant improvement in terms of objective and subjective performance while requiring only low computational overhead.","PeriodicalId":294532,"journal":{"name":"2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Enhanced motion estimation for driver assistance systems — Integration of a curved road model\",\"authors\":\"Gregor Schewior, H. Blume\",\"doi\":\"10.1109/GCCE.2013.6664897\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an extended efficient modelbased approach for the improvement of motion vector fields for driver assistance systems. Through the integration of a curved road model the quality of motion vector fields, estimated by a predictive block-based motion estimator working on video signals which are captured by a front camera inside a car, is significantly increased. The optimization step is performed by modeling synthetic motion vector fields of typical driving scenes acting in an iterative step as additional motion vector candidates. The proposed enhanced approach which is effective for all kinds of driving situations provides a significant improvement in terms of objective and subjective performance while requiring only low computational overhead.\",\"PeriodicalId\":294532,\"journal\":{\"name\":\"2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GCCE.2013.6664897\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCCE.2013.6664897","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Enhanced motion estimation for driver assistance systems — Integration of a curved road model
This paper presents an extended efficient modelbased approach for the improvement of motion vector fields for driver assistance systems. Through the integration of a curved road model the quality of motion vector fields, estimated by a predictive block-based motion estimator working on video signals which are captured by a front camera inside a car, is significantly increased. The optimization step is performed by modeling synthetic motion vector fields of typical driving scenes acting in an iterative step as additional motion vector candidates. The proposed enhanced approach which is effective for all kinds of driving situations provides a significant improvement in terms of objective and subjective performance while requiring only low computational overhead.