{"title":"基于极大似然估计框架的噪声和杂乱环境中多目标的同时检测和跟踪","authors":"R. Ilin, R. Deming","doi":"10.1109/OCEANSSYD.2010.5603524","DOIUrl":null,"url":null,"abstract":"We discuss a versatile framework for multiple target detection and tracking based on maximum likelihood estimation with expectation maximization and a cognitive theory called dynamic logic. In this contribution extend the framework to detection of moving objects in video sequences. The paper presents the theory and an example of detection and tracking using a real world video sequence.","PeriodicalId":129808,"journal":{"name":"OCEANS'10 IEEE SYDNEY","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Simultaneous detection and tracking of multiple objects in noisy and cluttered environment using maximum likelihood estimation framework\",\"authors\":\"R. Ilin, R. Deming\",\"doi\":\"10.1109/OCEANSSYD.2010.5603524\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We discuss a versatile framework for multiple target detection and tracking based on maximum likelihood estimation with expectation maximization and a cognitive theory called dynamic logic. In this contribution extend the framework to detection of moving objects in video sequences. The paper presents the theory and an example of detection and tracking using a real world video sequence.\",\"PeriodicalId\":129808,\"journal\":{\"name\":\"OCEANS'10 IEEE SYDNEY\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"OCEANS'10 IEEE SYDNEY\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OCEANSSYD.2010.5603524\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"OCEANS'10 IEEE SYDNEY","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OCEANSSYD.2010.5603524","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Simultaneous detection and tracking of multiple objects in noisy and cluttered environment using maximum likelihood estimation framework
We discuss a versatile framework for multiple target detection and tracking based on maximum likelihood estimation with expectation maximization and a cognitive theory called dynamic logic. In this contribution extend the framework to detection of moving objects in video sequences. The paper presents the theory and an example of detection and tracking using a real world video sequence.