@techreport{TD:101416,
	att_abstract={{The growing number of live video streams available today, combined with advances in system integration capabilities and video processing methods, provides a rich source of quantitative information that can act as a source stream for Big Data ingest to enable a wide range of new applications. While the primary application area for video analytics (VA) has been surveillance, some of the methods can be applied to entertainment video sources and consumer generated video as well. This e-letter will provide an overview of VA with a focus on distributed architecture considerations for practical deployment.}},
	att_authors={dg1597, lb6374},
	att_categories={},
	att_copyright={{}},
	att_copyright_notice={{}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={system on chip,  media processing,  smart camera},
	att_techdoc={true},
	att_techdoc_key={TD:101416},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101416_DS1_2014-04-29T01:23:04.643Z.pdf},
	author={David Gibbon and Lee Begeja},
	institution={{ IEEE Communications Society Multimedia Communications Technical Committee E-Letter}},
	month={May},
	title={{Distributed Processing for Big Data Video Analytics}},
	year=2014,
}