{"title":"多目标跟踪的学习粒子滤波","authors":"Lai-xiong Wang, Yangping Chen, Shitan Huang","doi":"10.1109/ICIA.2005.1635160","DOIUrl":null,"url":null,"abstract":"This paper presents a learning particle filter (LPF) to solve the problems of uncertainty, varying number, overlap, ambiguous, non-rigid, nonlinear, and non-Gaussian in tracking multiple visual targets. Evolution learning obtains a detector that guides proposal distribution originally, and then online learning renders the detector adaptive to pose altering and proposal distribution closer to posterior distribution. Simulation results demonstrate the performance of LPF algorithm.","PeriodicalId":136611,"journal":{"name":"2005 IEEE International Conference on Information Acquisition","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning particle filter for multiple target tracking\",\"authors\":\"Lai-xiong Wang, Yangping Chen, Shitan Huang\",\"doi\":\"10.1109/ICIA.2005.1635160\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a learning particle filter (LPF) to solve the problems of uncertainty, varying number, overlap, ambiguous, non-rigid, nonlinear, and non-Gaussian in tracking multiple visual targets. Evolution learning obtains a detector that guides proposal distribution originally, and then online learning renders the detector adaptive to pose altering and proposal distribution closer to posterior distribution. Simulation results demonstrate the performance of LPF algorithm.\",\"PeriodicalId\":136611,\"journal\":{\"name\":\"2005 IEEE International Conference on Information Acquisition\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 IEEE International Conference on Information Acquisition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIA.2005.1635160\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE International Conference on Information Acquisition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIA.2005.1635160","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning particle filter for multiple target tracking
This paper presents a learning particle filter (LPF) to solve the problems of uncertainty, varying number, overlap, ambiguous, non-rigid, nonlinear, and non-Gaussian in tracking multiple visual targets. Evolution learning obtains a detector that guides proposal distribution originally, and then online learning renders the detector adaptive to pose altering and proposal distribution closer to posterior distribution. Simulation results demonstrate the performance of LPF algorithm.