Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2015: (UTC)
Metadata Last Update2016: (UTC) administrator
Citation KeyNazaréJrFerrSchw:2015:ScVeFr
TitleA Scalable and Versatile Framework for Smart Video Surveillance
Access Date2022, Jan. 28
Secondary TypeMaster's Work
Number of Files1
Size961 KiB
Context area
Author1 Nazaré Jr., Antonio Carlos
2 Ferreira, Renato Antonio Celso
3 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Minas Gerais
EditorSegundo, Maurício Pamplona
Faria, Fabio Augusto
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2015-08-07 18:55:51 :: -> administrator ::
2016-06-03 21:18:38 :: administrator -> :: 2015
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Surveillance Systems
Computer Vision
Video Analysis
Video Surveillance
AbstractThe 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. > SDLA > SIBGRAPI 2015 > A Scalable and...
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Next Higher Units8JMKD3MGPBW34M/3K24PF8
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