{"title":"车辆检测与分类在Scala传感器中采用二值分类","authors":"Minho Cho, Baehoon Choi, Jhonghyun An, Euntai Kim","doi":"10.1109/ICCAS.2015.7364700","DOIUrl":null,"url":null,"abstract":"In this paper, we present approach for the detection and classification of multiple vehicles using a vehicle mounted laser scanner. The sensor, which is placed in front of the vehicle, is Scala 1403 and its characteristic is that it scans data twice -upper and lower direction- in one period. Consequently, it is difficult to detect and classify vehicles continuously. For solving this problem, another method for classification is needed. In this paper, binary classification is proposed for classification of Scala sensor. It can show better classification result than by using SVM (support vector machine) in case of occlusions. Experimental results carried out with laser range data illustrate the robustness of our approach.","PeriodicalId":6641,"journal":{"name":"2015 15th International Conference on Control, Automation and Systems (ICCAS)","volume":"104 1","pages":"2022-2025"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Vehicle detection and classification in the Scala sensor by using binary classification\",\"authors\":\"Minho Cho, Baehoon Choi, Jhonghyun An, Euntai Kim\",\"doi\":\"10.1109/ICCAS.2015.7364700\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present approach for the detection and classification of multiple vehicles using a vehicle mounted laser scanner. The sensor, which is placed in front of the vehicle, is Scala 1403 and its characteristic is that it scans data twice -upper and lower direction- in one period. Consequently, it is difficult to detect and classify vehicles continuously. For solving this problem, another method for classification is needed. In this paper, binary classification is proposed for classification of Scala sensor. It can show better classification result than by using SVM (support vector machine) in case of occlusions. Experimental results carried out with laser range data illustrate the robustness of our approach.\",\"PeriodicalId\":6641,\"journal\":{\"name\":\"2015 15th International Conference on Control, Automation and Systems (ICCAS)\",\"volume\":\"104 1\",\"pages\":\"2022-2025\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 15th International Conference on Control, Automation and Systems (ICCAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAS.2015.7364700\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 15th International Conference on Control, Automation and Systems (ICCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAS.2015.7364700","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Vehicle detection and classification in the Scala sensor by using binary classification
In this paper, we present approach for the detection and classification of multiple vehicles using a vehicle mounted laser scanner. The sensor, which is placed in front of the vehicle, is Scala 1403 and its characteristic is that it scans data twice -upper and lower direction- in one period. Consequently, it is difficult to detect and classify vehicles continuously. For solving this problem, another method for classification is needed. In this paper, binary classification is proposed for classification of Scala sensor. It can show better classification result than by using SVM (support vector machine) in case of occlusions. Experimental results carried out with laser range data illustrate the robustness of our approach.