{"title":"基于Mean Shift的视频目标跟踪改进方法","authors":"Nan Luo, Huan-Chun Xu, D. Xia","doi":"10.1109/ICONSCS.2012.6502465","DOIUrl":null,"url":null,"abstract":"This paper proposes an algorithm based on Mean Shift, which improves on the kernel function, alterative weights, combing with Kalman filter and neighborhood searching. These improvements not only enhance the capacity of target tracking, but also reduce the computations to satisfy the need of the real-time job. Furthermore, experimental results illuminate that the proposed algorithm can cope with clutter, target partial occlusions, scale variations and fast moving in the real-time video target tracking.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An improved approach to video target tracking based on Mean Shift\",\"authors\":\"Nan Luo, Huan-Chun Xu, D. Xia\",\"doi\":\"10.1109/ICONSCS.2012.6502465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an algorithm based on Mean Shift, which improves on the kernel function, alterative weights, combing with Kalman filter and neighborhood searching. These improvements not only enhance the capacity of target tracking, but also reduce the computations to satisfy the need of the real-time job. Furthermore, experimental results illuminate that the proposed algorithm can cope with clutter, target partial occlusions, scale variations and fast moving in the real-time video target tracking.\",\"PeriodicalId\":90521,\"journal\":{\"name\":\"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICONSCS.2012.6502465\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICONSCS.2012.6502465","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An improved approach to video target tracking based on Mean Shift
This paper proposes an algorithm based on Mean Shift, which improves on the kernel function, alterative weights, combing with Kalman filter and neighborhood searching. These improvements not only enhance the capacity of target tracking, but also reduce the computations to satisfy the need of the real-time job. Furthermore, experimental results illuminate that the proposed algorithm can cope with clutter, target partial occlusions, scale variations and fast moving in the real-time video target tracking.