{"title":"基于光流的改进Meanshift跟踪算法","authors":"Xiaoyan Yang, Qiu Li, Caijuan He","doi":"10.1109/ECEI57668.2023.10105361","DOIUrl":null,"url":null,"abstract":"The mean shift tracker has difficulty in tracking fast moving targets and suffers from local optimal problem. To overcome the limitation of the mean-shift tracking algorithm, a new approach is proposed by integrating the mean-shift algorithm and optical flow methods. Even with n the rough position, the mean-shift algorithm achieves precise tracking of the target. Several tracking experiments show that the proposed algorithm can effectively track fast moving target and overcome the tracking error cumulating problems.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved Meanshift Tracking Algorithm Based on Optical flow\",\"authors\":\"Xiaoyan Yang, Qiu Li, Caijuan He\",\"doi\":\"10.1109/ECEI57668.2023.10105361\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The mean shift tracker has difficulty in tracking fast moving targets and suffers from local optimal problem. To overcome the limitation of the mean-shift tracking algorithm, a new approach is proposed by integrating the mean-shift algorithm and optical flow methods. Even with n the rough position, the mean-shift algorithm achieves precise tracking of the target. Several tracking experiments show that the proposed algorithm can effectively track fast moving target and overcome the tracking error cumulating problems.\",\"PeriodicalId\":176611,\"journal\":{\"name\":\"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECEI57668.2023.10105361\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECEI57668.2023.10105361","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved Meanshift Tracking Algorithm Based on Optical flow
The mean shift tracker has difficulty in tracking fast moving targets and suffers from local optimal problem. To overcome the limitation of the mean-shift tracking algorithm, a new approach is proposed by integrating the mean-shift algorithm and optical flow methods. Even with n the rough position, the mean-shift algorithm achieves precise tracking of the target. Several tracking experiments show that the proposed algorithm can effectively track fast moving target and overcome the tracking error cumulating problems.