{"title":"基于视觉的自动驾驶车辆实时行人检测","authors":"Liu Xin, Dai Bin, He Hangen","doi":"10.1109/ICVES.2007.4456404","DOIUrl":null,"url":null,"abstract":"TMs paper presents a real-time single-frame pedestrian detection approach. Combining efficient interesting regions selection and proper SVM classifier, the method is applicable to the autonomous vehicles running on urban roads. Experiment results with test dataset extracted from real driving on urban roads are presented to illustrate the performance of this approach.","PeriodicalId":202772,"journal":{"name":"2007 IEEE International Conference on Vehicular Electronics and Safety","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Vision-based real-time pedestrian detection for autonomous vehicle\",\"authors\":\"Liu Xin, Dai Bin, He Hangen\",\"doi\":\"10.1109/ICVES.2007.4456404\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"TMs paper presents a real-time single-frame pedestrian detection approach. Combining efficient interesting regions selection and proper SVM classifier, the method is applicable to the autonomous vehicles running on urban roads. Experiment results with test dataset extracted from real driving on urban roads are presented to illustrate the performance of this approach.\",\"PeriodicalId\":202772,\"journal\":{\"name\":\"2007 IEEE International Conference on Vehicular Electronics and Safety\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Conference on Vehicular Electronics and Safety\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICVES.2007.4456404\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Vehicular Electronics and Safety","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICVES.2007.4456404","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Vision-based real-time pedestrian detection for autonomous vehicle
TMs paper presents a real-time single-frame pedestrian detection approach. Combining efficient interesting regions selection and proper SVM classifier, the method is applicable to the autonomous vehicles running on urban roads. Experiment results with test dataset extracted from real driving on urban roads are presented to illustrate the performance of this approach.
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