{"title":"一种新的运动检测阈值分割方法","authors":"Huimin Wu, Xiaoshi Zheng, Yanling Zhao, Na Li","doi":"10.1109/PACIIA.2008.193","DOIUrl":null,"url":null,"abstract":"The thresholding method is a fundamental part for motion detection and other advanced applications,so this step is very important, it must base on a reliable, effective method in order to access to the robustness of the computer intelligent video surveillance system. A new thresholding method which is simple and effective is proposed in the article. Firstly, we propose an imitating uni-Gaussian model thresholding method for motion detection, aiming at the disadvantages of this algorithm existed in the experiment, a predecessor's research is introduced, and then the second mixed thresholding method based on complementary advantages of the two aforesaid methods is proposed. Experiment results show that our final method can obtain clear and complete information of the moving object, and eliminate noise fundamentally for the scene at the same time. Motion detection with this thresholding method is accurate and real-time.","PeriodicalId":275193,"journal":{"name":"IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"A New Thresholding Method Applied to Motion Detection\",\"authors\":\"Huimin Wu, Xiaoshi Zheng, Yanling Zhao, Na Li\",\"doi\":\"10.1109/PACIIA.2008.193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The thresholding method is a fundamental part for motion detection and other advanced applications,so this step is very important, it must base on a reliable, effective method in order to access to the robustness of the computer intelligent video surveillance system. A new thresholding method which is simple and effective is proposed in the article. Firstly, we propose an imitating uni-Gaussian model thresholding method for motion detection, aiming at the disadvantages of this algorithm existed in the experiment, a predecessor's research is introduced, and then the second mixed thresholding method based on complementary advantages of the two aforesaid methods is proposed. Experiment results show that our final method can obtain clear and complete information of the moving object, and eliminate noise fundamentally for the scene at the same time. Motion detection with this thresholding method is accurate and real-time.\",\"PeriodicalId\":275193,\"journal\":{\"name\":\"IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PACIIA.2008.193\",\"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 Pacific-Asia Workshop on Computational Intelligence and Industrial Application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACIIA.2008.193","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Thresholding Method Applied to Motion Detection
The thresholding method is a fundamental part for motion detection and other advanced applications,so this step is very important, it must base on a reliable, effective method in order to access to the robustness of the computer intelligent video surveillance system. A new thresholding method which is simple and effective is proposed in the article. Firstly, we propose an imitating uni-Gaussian model thresholding method for motion detection, aiming at the disadvantages of this algorithm existed in the experiment, a predecessor's research is introduced, and then the second mixed thresholding method based on complementary advantages of the two aforesaid methods is proposed. Experiment results show that our final method can obtain clear and complete information of the moving object, and eliminate noise fundamentally for the scene at the same time. Motion detection with this thresholding method is accurate and real-time.