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		<citationkey>NazaréJrFerrSchw:2015:ScVeFr</citationkey>
		<title>A Scalable and Versatile Framework for Smart Video Surveillance</title>
		<format>On-line</format>
		<year>2015</year>
		<secondarytype>Master's Work</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>961 KiB</size>
		<author>Nazaré Jr., Antonio Carlos,</author>
		<author>Ferreira, Renato Antonio Celso,</author>
		<author>Schwartz, William Robson,</author>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<editor>Segundo, Maurício Pamplona,</editor>
		<editor>Faria, Fabio Augusto,</editor>
		<e-mailaddress>antonio.nazare@dcc.ufmg.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)</conferencename>
		<conferencelocation>Salvador</conferencelocation>
		<date>Aug. 26-29, 2015</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Master's or Doctoral Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Smart~Surveillance~Framework, Surveillance Systems, Computer Vision, Video Analysis, Video Surveillance.</keywords>
		<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.</abstract>
		<language>en</language>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2015/08.07.18.55</url>
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