{"title":"多目标模糊跟踪","authors":"L. Perlovsky","doi":"10.1117/12.138232","DOIUrl":null,"url":null,"abstract":"The authors have applied a previously developed MLANS neural network to the problem of tracking multiple objects in heavy clutter. In their approach the MLANS performs a fuzzy classification of all objects in multiple frames in multiple classes of tracks and random clutter. This novel approach to tracking using an optimal classification algorithm results in a dramatic improvement of performance: the MILANS tracking combines advantages of both the JPD and the MHT, it is capable of track initiation by considering multiple frames, and it eliminates combinatorial search via fuzzy associations.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"145 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Fuzzy tracking of multiple objects\",\"authors\":\"L. Perlovsky\",\"doi\":\"10.1117/12.138232\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors have applied a previously developed MLANS neural network to the problem of tracking multiple objects in heavy clutter. In their approach the MLANS performs a fuzzy classification of all objects in multiple frames in multiple classes of tracks and random clutter. This novel approach to tracking using an optimal classification algorithm results in a dramatic improvement of performance: the MILANS tracking combines advantages of both the JPD and the MHT, it is capable of track initiation by considering multiple frames, and it eliminates combinatorial search via fuzzy associations.<<ETX>>\",\"PeriodicalId\":354832,\"journal\":{\"name\":\"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop\",\"volume\":\"145 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.138232\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.138232","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The authors have applied a previously developed MLANS neural network to the problem of tracking multiple objects in heavy clutter. In their approach the MLANS performs a fuzzy classification of all objects in multiple frames in multiple classes of tracks and random clutter. This novel approach to tracking using an optimal classification algorithm results in a dramatic improvement of performance: the MILANS tracking combines advantages of both the JPD and the MHT, it is capable of track initiation by considering multiple frames, and it eliminates combinatorial search via fuzzy associations.<>
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