@InProceedings{OliveiraIntePereGome:2019:ViAuAn,
               author = "Oliveira, {\'{\I}}talo de Pontes and Interaminense, Carlos 
                         Daniel Oliveira and Pereira, Eanes Torres and Gomes, Herman 
                         Martins",
          affiliation = "UFCG and UFCG and UFCG and UFCG",
                title = "Video audience analysis using bayesian networks and face 
                         demographics",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Digital Signage, Computer Vision, Audience Analysis, Bayesian 
                         Networks, Face Analysis.",
             abstract = "In this paper, we propose an approach to study and to model 
                         audience attention to videos within digital signage scenarios. An 
                         experimental setup was conceived to simultaneously display videos 
                         of various categories and to capture videos from the audience and 
                         surrounding environment. Face analysis via a deep neural network 
                         is performed to estimate gender and age groups. In the proposed 
                         approach, a Bayesian Network is built to model possible 
                         relationships between the audience's age, gender and face size 
                         (which is indicative of the distance to the display) and the video 
                         content types. A publicly available video dataset of 152 videos 
                         was created for displaying purposes. An experimental evaluation 
                         indicated varying degrees of attention to different videos, 
                         depending on age and gender.The area under the ROC curve of the 
                         built Bayesian Network was 0.82. The proposed approach allows to 
                         better understand the possible relationships between audience 
                         demographics and video contents, which may, in turn, be useful for 
                         displaying the most appropriate content to a particular audience, 
                         help with the automatic insertion of ads (based on audience 
                         categories), among other applications.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00033",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00033",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U3AJKP",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U3AJKP",
           targetfile = "
                         
                         Modeling_Audience_to_Videos_Using_Bayesian_Networks_and_Facial_Analysis.pdf",
        urlaccessdate = "2024, Apr. 27"
}