@InProceedings{SantosPireColoPapa:2019:ViSeLe,
               author = "Santos, Daniel Felipe Silva and Pires, Rafael Gon{\c{c}}alves and 
                         Colombo, Danilo and Papa, Jo{\~a}o Paulo",
          affiliation = "S{\~a}o Paulo State University, Brazil and S{\~a}o Paulo State 
                         University, Brazil and {Petroleo Brasileiro S.A. - Petrobras} and 
                         S{\~a}o Paulo State University, Brazil",
                title = "Video Segmentation Learning Using Cascade Residual Convolutional 
                         Neural Network",
            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 = "Video Segmentation, Deep Learning, Foreground Object Detection, 
                         Residual Map.",
             abstract = "Video segmentation consists of a frame-by-frame selection process 
                         of meaningful areas related to foreground moving objects. Some 
                         applications include traffic monitoring, human tracking, action 
                         recognition, efficient video surveillance, and anomaly detection. 
                         In these applications, it is not rare to face challenges such as 
                         abrupt changes in weather conditions, illumination issues, 
                         shadows, subtle dynamic background motions, and also camouflage 
                         effects. In this work, we address such shortcomings by proposing a 
                         novel deep learning video segmentation approach that incorporates 
                         residual information into the foreground detection learning 
                         process. The main goal is to provide a method capable of 
                         generating an accurate foreground detection given a grayscale 
                         video. Experiments con- ducted on the Change Detection 2014 and on 
                         the private dataset PetrobrasROUTES from Petrobras support the 
                         effectiveness of the proposed approach concerning some 
                         state-of-the-art video segmentation techniques, with overall 
                         F-measures of 0.9535 and 0.9636 in the Change Detection 2014 and 
                         PetrobrasROUTES datasets, respectively. Such a result places the 
                         proposed technique amongst the top 3 state-of-the-art video 
                         segmentation methods, besides comprising approximately seven times 
                         less parameters than its top one counterpart.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00009",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00009",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U2N2QH",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U2N2QH",
           targetfile = "PID6127143.pdf",
        urlaccessdate = "2024, Apr. 28"
}