{"title":"基于分类标记多伯努利滤波的多摄像头红绿灯识别","authors":"Martin Bach, Stephan Reuter, K. Dietmayer","doi":"10.1109/IVS.2017.7995852","DOIUrl":null,"url":null,"abstract":"The correct handling of complex traffic-light-controlled intersections is still a challenge for automated vehicles. While a number of image-based approaches tackle close-range recognitions, an early traffic light detection at high distances is of great importance in the area of energy-efficient driving. For this reason, a traffic light detection system consisting of multiple on-board cameras is presented in this work, enabling the detection of traffic lights even from a distance of more than 200m. Furthermore, the presented system is based on tracking techniques using a Labeled Multi-Bernoulli filter in combination with the fusion of classifications based on the Dempster-Shafer theory of evidence. The system was tested on a real world data set collected in Germany and an increase in performance was demonstrated by a multi-camera approach.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"220-223 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Multi-camera traffic light recognition using a classifying Labeled Multi-Bernoulli filter\",\"authors\":\"Martin Bach, Stephan Reuter, K. Dietmayer\",\"doi\":\"10.1109/IVS.2017.7995852\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The correct handling of complex traffic-light-controlled intersections is still a challenge for automated vehicles. While a number of image-based approaches tackle close-range recognitions, an early traffic light detection at high distances is of great importance in the area of energy-efficient driving. For this reason, a traffic light detection system consisting of multiple on-board cameras is presented in this work, enabling the detection of traffic lights even from a distance of more than 200m. Furthermore, the presented system is based on tracking techniques using a Labeled Multi-Bernoulli filter in combination with the fusion of classifications based on the Dempster-Shafer theory of evidence. The system was tested on a real world data set collected in Germany and an increase in performance was demonstrated by a multi-camera approach.\",\"PeriodicalId\":143367,\"journal\":{\"name\":\"2017 IEEE Intelligent Vehicles Symposium (IV)\",\"volume\":\"220-223 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Intelligent Vehicles Symposium (IV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2017.7995852\",\"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 IEEE Intelligent Vehicles Symposium (IV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2017.7995852","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-camera traffic light recognition using a classifying Labeled Multi-Bernoulli filter
The correct handling of complex traffic-light-controlled intersections is still a challenge for automated vehicles. While a number of image-based approaches tackle close-range recognitions, an early traffic light detection at high distances is of great importance in the area of energy-efficient driving. For this reason, a traffic light detection system consisting of multiple on-board cameras is presented in this work, enabling the detection of traffic lights even from a distance of more than 200m. Furthermore, the presented system is based on tracking techniques using a Labeled Multi-Bernoulli filter in combination with the fusion of classifications based on the Dempster-Shafer theory of evidence. The system was tested on a real world data set collected in Germany and an increase in performance was demonstrated by a multi-camera approach.