{"title":"A Novel Algorithm for Fast Speckled Beacon Flicker Objects Detection in Complicated Environment","authors":"Haiying Liu, Xingyu Mu, Lixia Deng, Yang Zhao","doi":"10.1109/ICAICA52286.2021.9497905","DOIUrl":null,"url":null,"abstract":"A novel detection algorithm is proposed for solving the problems about fast moving speckled objections in the complicated background and to improve the properties of lower accuracy, real time and false detection rate and etc. In order to solve the problem of detecting beacon flicker under stray light interference especially for dynamic target detection system, we adopted the modified traditional background subtract method and classic mixed adjacency method. Experiments demonstrated the novel algorithm has the advantages of high detection accuracy, short response time and etc., and can be well applied in some natural and complicated environments.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAICA52286.2021.9497905","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A novel detection algorithm is proposed for solving the problems about fast moving speckled objections in the complicated background and to improve the properties of lower accuracy, real time and false detection rate and etc. In order to solve the problem of detecting beacon flicker under stray light interference especially for dynamic target detection system, we adopted the modified traditional background subtract method and classic mixed adjacency method. Experiments demonstrated the novel algorithm has the advantages of high detection accuracy, short response time and etc., and can be well applied in some natural and complicated environments.