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@InProceedings{SantosPireColoPapa:2020:ScChDe,
               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 (UNESP)} and {S{\~a}o Paulo 
                         State University (UNESP)} and {PETROBRAS - BR} and {S{\~a}o Paulo 
                         State University (UNESP)}",
                title = "Scene Change Detection Using Multiscale Cascade Residual 
                         Convolutional Neural Networks",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "change, detection, learning, multiscale.",
             abstract = "Scene change detection is an image processing problem related to 
                         partitioning pixels of a digital image into foreground and 
                         background regions. Mostly, visual knowledge-based computer 
                         intelligent systems, like traffic monitoring, video surveillance, 
                         and anomaly detection, need to use change detection techniques. 
                         Amongst the most prominent detection methods, there are the 
                         learning-based ones, which besides sharing similar training and 
                         testing protocols, differ from each other in terms of their 
                         architecture design strategies. Such architecture design directly 
                         impacts on the quality of the detection results, and also in the 
                         device resources capacity, like memory. In this work, we propose a 
                         novel Multiscale Cascade Residual Convolutional Neural Network 
                         that integrates multiscale processing strategy through a Residual 
                         Processing Module, with a Segmentation Convolutional Neural 
                         Network. Experiments conducted on two different datasets support 
                         the effectiveness of the proposed approach, achieving average 
                         overall F -measure results of 0.9622 and 0.9664 over Change 
                         Detection 2014 and PetrobrasROUTES datasets respectively, besides 
                         comprising approximately eight times fewer parameters. Such 
                         obtained results place the proposed technique amongst the top four 
                         state-of-the-art scene change detection methods.",
  conference-location = "Virtual",
      conference-year = "Nov. 7-10, 2020",
             language = "en",
           targetfile = "71.pdf",
        urlaccessdate = "2020, Dec. 04"
}


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