@InProceedings{AlmeidaVida:2015:ClNãEv,
author = "Almeida, Ana Paula Gon{\c{c}}alves S. de and Vidal, Flavio de
Barros",
affiliation = "{Programa de P{\'o}s-Gradua{\c{c}}{\~a}o em Sistemas
Mecatr{\^o}nicos - Universidade de Bras{\'{\i}}lia} and
{Departamento de Ci{\^e}ncia da Computa{\c{c}}{\~a}o -
Universidade de Brasilia}",
title = "Classifica{\c{c}}{\~a}o n{\~a}o-supervisionada de eventos em
imagens de videomonitoramento baseada em altas frequ{\^e}ncias do
fluxo {\'o}tico diferencial",
booktitle = "Proceedings...",
year = "2015",
editor = "Rios, Ricardo Araujo and Paiva, Afonso",
organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "event classification, motion analysis, optical flow.",
abstract = "Events related to vandalism and violence often occur in crowded
environments and mostly in unstructured environments (dynamic).
The use of security cameras in crowd monitoring analysis for
anomaly detection and alarms could be an efficient and inexpensive
method. The goal of this paper is to build an unsupervised
classification method to abnormal events that is robust and
stable. The main idea is based on analysis of Fourier Transform's
high-frequency spatial components features of optical flow,
allowing the detection of abnormal acts in surveillance videos.
Preliminary results show that the proposed methodology is capable
of successfully execute the process of detection, permitting the
development of an efficient recognition stage for future works.",
conference-location = "Salvador, BA, Brazil",
conference-year = "26-29 Aug. 2015",
language = "pt",
ibi = "8JMKD3MGPBW34M/3JRJS95",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JRJS95",
targetfile = "sibgrapi-cameraready.pdf",
urlaccessdate = "2024, Apr. 28"
}