{"title":"用于视频监控的一组摄像机的交互式姿态校准","authors":"Gaetano Manzo, F. Serratosa, M. Vento","doi":"10.1109/ETFA.2016.7733663","DOIUrl":null,"url":null,"abstract":"There has been an increase of video surveillance systems in operation in public areas. The classical systems simply send the images to monitors. Nevertheless, there is a demand on giving more intelligence on these systems and asking them to automatically track objects or recognise people. One of the basic low-level tasks that these systems have to face with is the accurate deduction of the cameras' poses. We present a method that deducts these poses in an interactive way when the automatic method fails or generates a large error. The user is asked for mapping some points between the images from these cameras when the alignment between them fails in a completely automatic way. Experimental validation has demonstrated that with really few interactions, the reduction of the pose error is considerable.","PeriodicalId":6483,"journal":{"name":"2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Interactive pose calibration of a set of cameras for video surveillance\",\"authors\":\"Gaetano Manzo, F. Serratosa, M. Vento\",\"doi\":\"10.1109/ETFA.2016.7733663\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There has been an increase of video surveillance systems in operation in public areas. The classical systems simply send the images to monitors. Nevertheless, there is a demand on giving more intelligence on these systems and asking them to automatically track objects or recognise people. One of the basic low-level tasks that these systems have to face with is the accurate deduction of the cameras' poses. We present a method that deducts these poses in an interactive way when the automatic method fails or generates a large error. The user is asked for mapping some points between the images from these cameras when the alignment between them fails in a completely automatic way. Experimental validation has demonstrated that with really few interactions, the reduction of the pose error is considerable.\",\"PeriodicalId\":6483,\"journal\":{\"name\":\"2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)\",\"volume\":\"1 1\",\"pages\":\"1-4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETFA.2016.7733663\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETFA.2016.7733663","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interactive pose calibration of a set of cameras for video surveillance
There has been an increase of video surveillance systems in operation in public areas. The classical systems simply send the images to monitors. Nevertheless, there is a demand on giving more intelligence on these systems and asking them to automatically track objects or recognise people. One of the basic low-level tasks that these systems have to face with is the accurate deduction of the cameras' poses. We present a method that deducts these poses in an interactive way when the automatic method fails or generates a large error. The user is asked for mapping some points between the images from these cameras when the alignment between them fails in a completely automatic way. Experimental validation has demonstrated that with really few interactions, the reduction of the pose error is considerable.