{"title":"协同群体检测与跟踪的模型与算法","authors":"S. K. Pang, Jack Li, S. Godsill","doi":"10.1109/AERO.2008.4526445","DOIUrl":null,"url":null,"abstract":"In this paper, we describe a set of models and algorithms for detection and tracking of group and individual targets. We develop a novel group dynamical model within a continuous time setting and a group structure transition model. This is combined with an interaction model using Markov Random Fields (MRF) to create a realistic group model. We use a Markov Chain Monte Carlo (MCMC)-Particle Algorithm to perform the sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets, as well as infer the correct group structure.","PeriodicalId":112420,"journal":{"name":"2007 5th International Symposium on Image and Signal Processing and Analysis","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"47","resultStr":"{\"title\":\"Models and Algorithms for Detection and Tracking of Coordinated Groups\",\"authors\":\"S. K. Pang, Jack Li, S. Godsill\",\"doi\":\"10.1109/AERO.2008.4526445\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we describe a set of models and algorithms for detection and tracking of group and individual targets. We develop a novel group dynamical model within a continuous time setting and a group structure transition model. This is combined with an interaction model using Markov Random Fields (MRF) to create a realistic group model. We use a Markov Chain Monte Carlo (MCMC)-Particle Algorithm to perform the sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets, as well as infer the correct group structure.\",\"PeriodicalId\":112420,\"journal\":{\"name\":\"2007 5th International Symposium on Image and Signal Processing and Analysis\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"47\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 5th International Symposium on Image and Signal Processing and Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AERO.2008.4526445\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 5th International Symposium on Image and Signal Processing and Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AERO.2008.4526445","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Models and Algorithms for Detection and Tracking of Coordinated Groups
In this paper, we describe a set of models and algorithms for detection and tracking of group and individual targets. We develop a novel group dynamical model within a continuous time setting and a group structure transition model. This is combined with an interaction model using Markov Random Fields (MRF) to create a realistic group model. We use a Markov Chain Monte Carlo (MCMC)-Particle Algorithm to perform the sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets, as well as infer the correct group structure.