Hu Tian, Bojin Zhuang, Yan Hua, Yanyun Zhao, A. Cai
{"title":"Recovering depth of background and foreground from a monocular video with camera motion","authors":"Hu Tian, Bojin Zhuang, Yan Hua, Yanyun Zhao, A. Cai","doi":"10.1109/VCIP.2013.6706409","DOIUrl":null,"url":null,"abstract":"In this paper we propose a depth recovery approach for monocular videos with or without camera motion. By combining geometric information and moving object extraction, not only the depth of background but also the depth of foreground can be recovered. Furthermore, for cases involving complex camera motion such as fast moving, translating, vertical movement, we propose a novel global motion estimation (GME) method including effective outlier rejection to extract moving objects, and experiments demonstrate that the proposed GME method outperforms most of the state-of-the-art methods. The depth recovery approach we propose is tested on four video sequences with different camera movements. Experimental results show that our approach produces more accurate depth of both background and foreground than existing depth recovery methods.","PeriodicalId":407080,"journal":{"name":"2013 Visual Communications and Image Processing (VCIP)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Visual Communications and Image Processing (VCIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VCIP.2013.6706409","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper we propose a depth recovery approach for monocular videos with or without camera motion. By combining geometric information and moving object extraction, not only the depth of background but also the depth of foreground can be recovered. Furthermore, for cases involving complex camera motion such as fast moving, translating, vertical movement, we propose a novel global motion estimation (GME) method including effective outlier rejection to extract moving objects, and experiments demonstrate that the proposed GME method outperforms most of the state-of-the-art methods. The depth recovery approach we propose is tested on four video sequences with different camera movements. Experimental results show that our approach produces more accurate depth of both background and foreground than existing depth recovery methods.