{"title":"基于人的图像间单应性自动估计","authors":"M. Thaler, R. Mörzinger","doi":"10.1109/AVSS.2010.35","DOIUrl":null,"url":null,"abstract":"Inter-image homographies are essential for many differenttasks involving projective geometry. This paper proposesan adaptive correspondence estimation approach betweenperson detections in a planar scene not relying oncorrespondence features as it is the case in many otherRANSAC-based approaches. The result is a planar interimagehomography calculated from estimated point correspondences.The approach is self-configurable, adaptiveand provides robustness over time by exploiting temporaland geometric information. We demonstrate the manifoldapplicability of the proposed approach on a variety ofdatasets. Improved results compared to a common baselineapproach are shown and the influence of error sources suchas missed detections, false detections and non overlappingfield of views is investigated.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Automatic Inter-image Homography Estimation from Person Detections\",\"authors\":\"M. Thaler, R. Mörzinger\",\"doi\":\"10.1109/AVSS.2010.35\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Inter-image homographies are essential for many differenttasks involving projective geometry. This paper proposesan adaptive correspondence estimation approach betweenperson detections in a planar scene not relying oncorrespondence features as it is the case in many otherRANSAC-based approaches. The result is a planar interimagehomography calculated from estimated point correspondences.The approach is self-configurable, adaptiveand provides robustness over time by exploiting temporaland geometric information. We demonstrate the manifoldapplicability of the proposed approach on a variety ofdatasets. Improved results compared to a common baselineapproach are shown and the influence of error sources suchas missed detections, false detections and non overlappingfield of views is investigated.\",\"PeriodicalId\":415758,\"journal\":{\"name\":\"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"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.2010.35\",\"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.2010.35","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Inter-image Homography Estimation from Person Detections
Inter-image homographies are essential for many differenttasks involving projective geometry. This paper proposesan adaptive correspondence estimation approach betweenperson detections in a planar scene not relying oncorrespondence features as it is the case in many otherRANSAC-based approaches. The result is a planar interimagehomography calculated from estimated point correspondences.The approach is self-configurable, adaptiveand provides robustness over time by exploiting temporaland geometric information. We demonstrate the manifoldapplicability of the proposed approach on a variety ofdatasets. Improved results compared to a common baselineapproach are shown and the influence of error sources suchas missed detections, false detections and non overlappingfield of views is investigated.