@InProceedings{ColqueJúniSchw: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, BA, Brazil",
conference-year = "26-29 Aug. 2015",
doi = "10.1109/SIBGRAPI.2015.21",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2015.21",
language = "en",
ibi = "8JMKD3MGPBW34M/3JLJ6HE",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JLJ6HE",
targetfile = "paper_camera_ready.pdf",
urlaccessdate = "2024, May 03"
}