@InProceedings{NazaréJrFerrSchw:2015:ScVeFr,
author = "Nazar{\'e} Jr., Antonio Carlos and Ferreira, Renato Antonio Celso
and Schwartz, William Robson",
affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal
de Minas Gerais} and {Universidade Federal de Minas Gerais}",
title = "A Scalable and Versatile Framework for Smart Video Surveillance",
booktitle = "Proceedings...",
year = "2015",
editor = "Segundo, Maur{\'{\i}}cio Pamplona and Faria, Fabio Augusto",
organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "Smart~Surveillance~Framework, Surveillance Systems, Computer
Vision, Video Analysis, Video Surveillance.",
abstract = "The large amount of visual data generated by surveillance cameras
is usually analyzed manually, a challenging task which is labor
intensive and prone to errors. Therefore, automatic approaches
must be employed to enable the proper processing of the visual
data. The main goal of automated surveillance systems is to
analyze the scene focusing on the detection and recognition of
suspicious activities. However, these systems are rarely tackled
in a scalable manner. With that in mind, this Masters thesis
proposed a framework for scalable video analysis called Smart
Surveillance Framework (SSF) to allow researchers to implement
their solutions to the surveillance problems as a sequence of
processing modules that communicate through a shared memory. The
framework provides useful features to the researchers, such as
memory management to allow handling large amounts of data,
communication control among execution modules, predefined data
structures specifically designed for the surveillance environment
and management of multiple data input. Our experimental results
evaluate important aspects of the Smart Surveillance Framework
(SSF) and demonstrate the scalability of the framework, the lower
overhead caused by the communication between the modules and the
shared memory and the high performance of our feature extraction
mechanism.",
conference-location = "Salvador, BA, Brazil",
conference-year = "26-29 Aug. 2015",
language = "en",
ibi = "8JMKD3MGPBW34M/3K2MKA8",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3K2MKA8",
targetfile = "article.pdf",
urlaccessdate = "2024, May 02"
}