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@InProceedings{ColqueJ˙niSchw:2015:HiOpFl,
               author = "Colque, Rensso Victor Hugo Mora and J{\'u}nior, Carlos 
                         Ant{\^o}nio Caetano and Schwartz, William Robson",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais} and {Universidade Federal de Minas Gerais}",
                title = "Histograms of Optical Flow Orientation and Magnitude to Detect 
                         Anomalous Events in Videos",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Anomalous event detection, spatiotemporal feature extraction, 
                         optical flow, histograms of oriented optical flow, smart 
                         surveillance.",
             abstract = "Modeling human behavior and activity patterns for recognition or 
                         detection of anomalous events has attracted significant research 
                         interest in recent years, particularly among the video 
                         surveillance community. An anomalous event might be characterized 
                         as an event that deviates from the normal or usual, but not 
                         necessarily in an undesirable manner, e.g., an anomalous event 
                         might just be different from normal but not a suspicious event 
                         from the surveillance stand point. One of the main challenges of 
                         detecting such events is the difficulty to create models due to 
                         their unpredictability. Therefore, most works model the expected 
                         patterns on the scene, instead, based on video sequences where 
                         anomalous events do not occur. Assuming images captured from a 
                         single camera, we propose a novel spatiotemporal feature 
                         descriptor, called \emph{Histograms of Optical Flow Orientation 
                         and Magnitude} (HOFM), based on optical flow information to 
                         describe the normal patterns on the scene, so that we can employ a 
                         simple nearest neighbor search to identify whether a given unknown 
                         pattern should be classified as an anomalous event. Our descriptor 
                         captures spatiotemporal information from cuboids (regions with 
                         spatial and temporal support) and encodes both magnitude and 
                         orientation of the optical flow separately into histograms, 
                         differently from previous works, which are based only on the 
                         orientation. The experimental evaluation demonstrates that our 
                         approach is able to detect anomalous events with success, 
                         achieving better results than the descriptor based only on optical 
                         flow orientation and outperforming several state-of-the-art 
                         methods on one scenario (Peds2) of the well-known UCSD anomaly 
                         data set, and achieving comparable results in the other scenario 
                         (Peds1).",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
           targetfile = "paper_camera_ready.pdf",
        urlaccessdate = "2021, Nov. 28"
}


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