@InProceedings{CarneiroSilvGuimPedr:2019:FiDeVi,
               author = "Carneiro, Sarah Almeida and Silva, Gabriel Pellegrino da and 
                         Guimar{\~a}es, Silvio Jamil F. and Pedrini, Helio",
          affiliation = "Institute of Computing, University of Campinas and Institute of 
                         Computing, University of Campinas and Computer Science Department, 
                         Pontifical Catholic University of Minas Gerais and Institute of 
                         Computing, University of Campinas",
                title = "Fight Detection in Video Sequences Based on Multi-Stream 
                         Convolutional Neural Networks",
            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 = "Fight detection, convolutional neural networks, video analysis.",
             abstract = "Surveillance has been gradually correlating itself to forensic 
                         computer technologies. The use of machine learning techniques made 
                         possible the better interpretation of human actions, as well as 
                         faster identification of anomalous event outbursts. There are many 
                         studies regarding this field of expertise. The best results 
                         reported in the literature are from works related to deep learning 
                         approaches. Therefore, this study aimed to use a deep learning 
                         model based on a multi-stream and high level hand-crafted 
                         descriptors to be able to address the issue of fight detection in 
                         videos. In this work, we focused on the use of a multi-stream of 
                         VGG-16 networks and the investigation of conceivable feature 
                         descriptors of a video's spatial, temporal, rhythmic and depth 
                         information. We validated our method in two commonly used 
                         datasets, aimed at fight detection, throughout the literature. 
                         Experimentation has demonstrated that the association of 
                         correlated information with a multi-stream strategy increased the 
                         classification of our deep learning approach, hence, the use of 
                         complementary features can yield interesting outputs that are 
                         superior than other previous studies.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00010",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00010",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U2DL8E",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U2DL8E",
           targetfile = "paper.pdf",
        urlaccessdate = "2024, Apr. 27"
}