{"title":"基于轨迹数据的驾驶员行为分类","authors":"M. Rigolli, Q. Williams, M. Gooding, M. Brady","doi":"10.1109/ITSC.2005.1520168","DOIUrl":null,"url":null,"abstract":"In recent years, traffic video surveillance has increased significantly. However, most of the footage is reviewed by humans or not at all. Tools capable of analysing traffic video sequences and autonomously extracting information are required. This paper presents an analysis of two automatic methods for classifying driver behaviour using only data provided by vehicle trackers. The algorithms are tested on several simulated traffic situations and their performance is compared to human observers. Factor analysis is shown to outperform human observers. We believe this is the first time automatic behavioural clustering of drivers using trajectory information has been successfully demonstrated.","PeriodicalId":153203,"journal":{"name":"Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Driver behavioural classification from trajectory data\",\"authors\":\"M. Rigolli, Q. Williams, M. Gooding, M. Brady\",\"doi\":\"10.1109/ITSC.2005.1520168\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, traffic video surveillance has increased significantly. However, most of the footage is reviewed by humans or not at all. Tools capable of analysing traffic video sequences and autonomously extracting information are required. This paper presents an analysis of two automatic methods for classifying driver behaviour using only data provided by vehicle trackers. The algorithms are tested on several simulated traffic situations and their performance is compared to human observers. Factor analysis is shown to outperform human observers. We believe this is the first time automatic behavioural clustering of drivers using trajectory information has been successfully demonstrated.\",\"PeriodicalId\":153203,\"journal\":{\"name\":\"Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005.\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITSC.2005.1520168\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSC.2005.1520168","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Driver behavioural classification from trajectory data
In recent years, traffic video surveillance has increased significantly. However, most of the footage is reviewed by humans or not at all. Tools capable of analysing traffic video sequences and autonomously extracting information are required. This paper presents an analysis of two automatic methods for classifying driver behaviour using only data provided by vehicle trackers. The algorithms are tested on several simulated traffic situations and their performance is compared to human observers. Factor analysis is shown to outperform human observers. We believe this is the first time automatic behavioural clustering of drivers using trajectory information has been successfully demonstrated.