Jia Hao, Guanfeng Wang, Beomjoo Seo, Roger Zimmermann
{"title":"Keyframe presentation for browsing of user-generated videos on map interfaces","authors":"Jia Hao, Guanfeng Wang, Beomjoo Seo, Roger Zimmermann","doi":"10.1145/2072298.2071926","DOIUrl":null,"url":null,"abstract":"To present user-generated videos that relate to geographic areas for easy access and browsing it is often natural to use maps as interfaces. A common approach is to place thumbnail images of video keyframes in appropriate locations. Here we consider the challenge of determining which keyframes to select and where to place them on the map. Our proposed technique leverages sensor-collected meta-data which are automatically acquired as a continuous stream together with the video. Our approach is able to detect interesting regions and objects (hotspots) and their distances from the camera in a fully automated way. Meaningful keyframes are adaptively selected based on the popularity of the hotspots. Our experiments show very promising results and demonstrate excellent utility for the users.","PeriodicalId":318758,"journal":{"name":"Proceedings of the 19th ACM international conference on Multimedia","volume":"146 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 19th ACM international conference on Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2072298.2071926","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
To present user-generated videos that relate to geographic areas for easy access and browsing it is often natural to use maps as interfaces. A common approach is to place thumbnail images of video keyframes in appropriate locations. Here we consider the challenge of determining which keyframes to select and where to place them on the map. Our proposed technique leverages sensor-collected meta-data which are automatically acquired as a continuous stream together with the video. Our approach is able to detect interesting regions and objects (hotspots) and their distances from the camera in a fully automated way. Meaningful keyframes are adaptively selected based on the popularity of the hotspots. Our experiments show very promising results and demonstrate excellent utility for the users.