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This paper presents a sparse signal representation based approach to address the problem of human action recognition in videos. For each action, a set of redundant basis (dictionary) is learnt by solving a sparse optimization problem. A dictionary is learnt using the image patches of its corresponding action, such that every patch vector is represented by some linear combination of a small number of basis vectors. By learning one dictionary per action, it is expected that each dictionary candoi:10.1109/fg.2011.5771388 dblp:conf/fgr/GuhaW11 fatcat:uxbtmlav6baa5dongsu6p34n2u