@InProceedings{SouzaPedr:2017:DeViEv,
               author = "Souza, Felipe de and Pedrini, Helio",
          affiliation = "Institute of Computing, University of Campinas (UNICAMP) and 
                         Institute of Computing, University of Campinas (UNICAMP)",
                title = "Detection of Violent Events in Video Sequences based on Census 
                         Transform Histogram",
            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 = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "video analysis, violent detection, surveillance systems, anomalous 
                         events.",
             abstract = "Video surveillance systems have enabled the monitoring of complex 
                         events in several places, such as airports, banks, streets, 
                         schools, industries, among others. Due to the massive amount of 
                         multimedia data acquired by video cameras, traditional visual 
                         inspection by human operators is a very tedious and time consuming 
                         task, whose performance is affected by fatigue and stress. A 
                         challenge is to develop intelligent video systems capable of 
                         automatically analyzing long sequences of videos from a large 
                         number of cameras. This work describes and evaluates the use of 
                         CENTRIST-based features to identify violence context from video 
                         scenes. Experimental results demonstrate the effectiveness of our 
                         method when applied to two public benchmarks, Violent Flows and 
                         Hockey Fights datasets.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.49",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.49",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFGBPP",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFGBPP",
           targetfile = "paper.pdf",
        urlaccessdate = "2024, May 02"
}