{"title":"三维场景结构的语义标注提取和未编辑视频的检索","authors":"I. Feldmann, W. Waizenegger, O. Schreer","doi":"10.1109/MMSP.2008.4665053","DOIUrl":null,"url":null,"abstract":"In this paper we discuss the application of 3D scene reconstruction techniques in the area of automatic semantic annotation, search and retrieval of unedited video footage. Rather than working with static key-frames we exploit the time-depended dynamic properties of a moving camera. Based on state of the art camera self calibration techniques we develop a powerful analysis chain. We demonstrate, that the reconstructed 3D scene information can be used to generate both, accurate low level scene descriptors as well as meaningful medium and high level semantic information. We show, that the proposed algorithms work even in case of sparse data sets. The proposed algorithms provide a powerful working base for further investigations in the area of low, medium and high level extraction of semantic information for unedited video.","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Extraction of 3D scene structure for semantic annotation and retrieval of unedited video\",\"authors\":\"I. Feldmann, W. Waizenegger, O. Schreer\",\"doi\":\"10.1109/MMSP.2008.4665053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we discuss the application of 3D scene reconstruction techniques in the area of automatic semantic annotation, search and retrieval of unedited video footage. Rather than working with static key-frames we exploit the time-depended dynamic properties of a moving camera. Based on state of the art camera self calibration techniques we develop a powerful analysis chain. We demonstrate, that the reconstructed 3D scene information can be used to generate both, accurate low level scene descriptors as well as meaningful medium and high level semantic information. We show, that the proposed algorithms work even in case of sparse data sets. The proposed algorithms provide a powerful working base for further investigations in the area of low, medium and high level extraction of semantic information for unedited video.\",\"PeriodicalId\":402287,\"journal\":{\"name\":\"2008 IEEE 10th Workshop on Multimedia Signal Processing\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE 10th Workshop on Multimedia Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMSP.2008.4665053\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE 10th Workshop on Multimedia Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMSP.2008.4665053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extraction of 3D scene structure for semantic annotation and retrieval of unedited video
In this paper we discuss the application of 3D scene reconstruction techniques in the area of automatic semantic annotation, search and retrieval of unedited video footage. Rather than working with static key-frames we exploit the time-depended dynamic properties of a moving camera. Based on state of the art camera self calibration techniques we develop a powerful analysis chain. We demonstrate, that the reconstructed 3D scene information can be used to generate both, accurate low level scene descriptors as well as meaningful medium and high level semantic information. We show, that the proposed algorithms work even in case of sparse data sets. The proposed algorithms provide a powerful working base for further investigations in the area of low, medium and high level extraction of semantic information for unedited video.