@techreport{TD:101130,
	att_abstract={{While the notion of joint sparsity in understanding common
and innovative components of a multi-receiver signal
ensemble has been well studied, we investigate the utility of
such joint sparse models in representing information contained
in a single video signal. By decomposing the content
of a video sequence into that observed by multiple spatially
and/or temporally distributed receivers, we first recover
a collection of common and innovative components
pertaining to individual videos. We then present modeling
strategies based on subspace-driven manifold metrics
to characterize patterns among these components, across
other videos in the system, to perform subsequent video
analysis. We demonstrate the efficacy of our approach for
activity classification and clustering by reporting competitive
results on standard datasets such as, HMDB, UCF-50,
Olympic Sports and KTH.}},
	att_authors={rg4675},
	att_categories={},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2013. {{, 2013-06-25}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101130},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101130_DS1_2013-07-01T18:10:30.452Z.pdf},
	author={Raghuraman Gopalan},
	institution={{IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2013}},
	month={June},
	title={{Joint Sparsity-based Representation and Analysis of Unconstrained Activities}},
	year=2013,
}