{"title":"一种基于像素纹理相关背景模型的视频目标检测算法","authors":"Jibin Fu, Xin Bai, Baode Ju","doi":"10.1109/IVSURV.2011.6157025","DOIUrl":null,"url":null,"abstract":"This paper describes a screen target detection algorithm which is based on the background of Pixel texture information to judge models. The model is based on Bayesian statistical model frame, using histogram to get background reference, and lead in texture information of pixels to determine the results of the test optimization. It can quickly and accurately generate reference background, accumulating less noise during the period of updating background, and keeping longtime stability. Experimental results show that compared with the Bayesian statistical model, the screen target detection algorithm which is based on the background of Pixel texture information to judge models has greatly improved in accuracy and reduced in error rate.","PeriodicalId":141829,"journal":{"name":"2011 Third Chinese Conference on Intelligent Visual Surveillance","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A video target detection algorithm based on pixels texture correlation background model\",\"authors\":\"Jibin Fu, Xin Bai, Baode Ju\",\"doi\":\"10.1109/IVSURV.2011.6157025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a screen target detection algorithm which is based on the background of Pixel texture information to judge models. The model is based on Bayesian statistical model frame, using histogram to get background reference, and lead in texture information of pixels to determine the results of the test optimization. It can quickly and accurately generate reference background, accumulating less noise during the period of updating background, and keeping longtime stability. Experimental results show that compared with the Bayesian statistical model, the screen target detection algorithm which is based on the background of Pixel texture information to judge models has greatly improved in accuracy and reduced in error rate.\",\"PeriodicalId\":141829,\"journal\":{\"name\":\"2011 Third Chinese Conference on Intelligent Visual Surveillance\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Third Chinese Conference on Intelligent Visual Surveillance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVSURV.2011.6157025\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Third Chinese Conference on Intelligent Visual Surveillance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVSURV.2011.6157025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A video target detection algorithm based on pixels texture correlation background model
This paper describes a screen target detection algorithm which is based on the background of Pixel texture information to judge models. The model is based on Bayesian statistical model frame, using histogram to get background reference, and lead in texture information of pixels to determine the results of the test optimization. It can quickly and accurately generate reference background, accumulating less noise during the period of updating background, and keeping longtime stability. Experimental results show that compared with the Bayesian statistical model, the screen target detection algorithm which is based on the background of Pixel texture information to judge models has greatly improved in accuracy and reduced in error rate.