{"title":"An Intelligence Monitoring System for Abnormal Water Surface Based on ART","authors":"Youfu Wu, Jianjun Zhuo, Jing Wu","doi":"10.1109/ICDMA.2013.40","DOIUrl":null,"url":null,"abstract":"Classing object is an important step for the high-level visions processing tasks, such as security managing, and abnormality event analysis. In this paper, we address these challenges of abnormal water surface monitoring in real-world unconstrained environments where the background is complex and dynamic. In the algorithm proposed, we extract the moment features of water surface in a color space, and a technique is developed to monitor the abnormal surface of water based on ART. Experimental results show that our algorithm works efficiently and robustly.","PeriodicalId":403312,"journal":{"name":"2013 Fourth International Conference on Digital Manufacturing & Automation","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Fourth International Conference on Digital Manufacturing & Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMA.2013.40","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Classing object is an important step for the high-level visions processing tasks, such as security managing, and abnormality event analysis. In this paper, we address these challenges of abnormal water surface monitoring in real-world unconstrained environments where the background is complex and dynamic. In the algorithm proposed, we extract the moment features of water surface in a color space, and a technique is developed to monitor the abnormal surface of water based on ART. Experimental results show that our algorithm works efficiently and robustly.