{"title":"统计背景建模:一种基于边缘段的运动目标检测方法","authors":"M. Murshed, Adín Ramírez Rivera, O. Chae","doi":"10.1109/AVSS.2010.18","DOIUrl":null,"url":null,"abstract":"We propose an edge segment based statistical backgroundmodeling algorithm and a moving edge detectionframework for the detection of moving objects. We analyzethe performance of the proposed segment based statisticalbackground model with traditional pixel based, edge pixelbased and edge segment based approaches. Existing edgebased moving object detection algorithms fetches difficultydue to the change in background motion, object shape, illuminationvariation and noise. The proposed algorithmmakes efficient use of statistical background model usingthe edge-segment structure. Experiments with natural imagesequences show that our method can detect moving objectsefficiently under the above mentioned environments.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":"{\"title\":\"Statistical Background Modeling: An Edge Segment Based Moving Object Detection Approach\",\"authors\":\"M. Murshed, Adín Ramírez Rivera, O. Chae\",\"doi\":\"10.1109/AVSS.2010.18\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose an edge segment based statistical backgroundmodeling algorithm and a moving edge detectionframework for the detection of moving objects. We analyzethe performance of the proposed segment based statisticalbackground model with traditional pixel based, edge pixelbased and edge segment based approaches. Existing edgebased moving object detection algorithms fetches difficultydue to the change in background motion, object shape, illuminationvariation and noise. The proposed algorithmmakes efficient use of statistical background model usingthe edge-segment structure. Experiments with natural imagesequences show that our method can detect moving objectsefficiently under the above mentioned environments.\",\"PeriodicalId\":415758,\"journal\":{\"name\":\"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AVSS.2010.18\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AVSS.2010.18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Statistical Background Modeling: An Edge Segment Based Moving Object Detection Approach
We propose an edge segment based statistical backgroundmodeling algorithm and a moving edge detectionframework for the detection of moving objects. We analyzethe performance of the proposed segment based statisticalbackground model with traditional pixel based, edge pixelbased and edge segment based approaches. Existing edgebased moving object detection algorithms fetches difficultydue to the change in background motion, object shape, illuminationvariation and noise. The proposed algorithmmakes efficient use of statistical background model usingthe edge-segment structure. Experiments with natural imagesequences show that our method can detect moving objectsefficiently under the above mentioned environments.