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@InProceedings{RamosCampNasc:2017:SeHyEg,
               author = "Ramos, Washington Luis de Souza and Campos, Mario Fernando 
                         Montenegro and Nascimento, Erickson Rangel do",
          affiliation = "{Universidade Federal de Minas Gerais (UFMG)} and {Universidade 
                         Federal de Minas Gerais (UFMG)} and {Universidade Federal de Minas 
                         Gerais (UFMG)}",
                title = "Semantic Hyperlapse for Egocentric Videos",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Hyperlapse, Fast-forward, Semantic information, First-person 
                         video.",
             abstract = "The emergence of low-cost personal mobiles devices and wearable 
                         cameras and, the increasing storage capacity of video-sharing 
                         websites have pushed forward a growing interest towards 
                         first-person videos. Wearable cameras can operate for hours 
                         without the need for continuous handling. These videos are 
                         generally long-running streams with unedited content, which makes 
                         them boring and visually unpalatable since the natural body 
                         movements cause the videos to be jerky and even nauseating. 
                         Hyperlapse algorithms aim to create a shorter watchable version 
                         with no abrupt transitions between the frames. However, an 
                         important aspect of such videos is the relevance of the frames, 
                         usually ignored in hyperlapse videos. In this work, we propose a 
                         novel methodology capable of summarizing and stabilizing 
                         egocentric videos by extracting and analyzing the semantic 
                         information in the frames. This work also describes a dataset 
                         collection with several labeled videos and introduces a new 
                         smoothness evaluation metric for egocentric videos. Several 
                         experiments are conducted to show the superiority of our approach 
                         over the state-of-the-art hyperlapse algorithms as far as semantic 
                         information is concerned. According to the results, our method is 
                         on average 10.67 percentage points higher than the second best in 
                         relation to the maximum amount of semantics that can be obtained, 
                         given the required speed-up. More information can be found in our 
                         supplementary video: https://youtu.be/_TU8KPaA8aU.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PJJ6JE",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PJJ6JE",
           targetfile = "2017_wtd_sibgrapi.pdf",
        urlaccessdate = "2021, Jan. 21"
}


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