Joint Sparsity-Based Representation and Analysis of Unconstrained Activities

CVPR 2013 Raghuraman Gopalan

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... (read more)

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