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Human action recognition based on sparse representation induced by L1/L2 regulations

Sparse representation based classification (SRC) has been widely used for face recognition (FR). Although SRC algorithm is also adopted in human action recognition, the evaluations of different regular terms have not been given. In this paper, we will discuss and evaluate the role of different regular terms of SRC in human action recognition, after that, we propose human action recognition algorithm based on sparse representation induced by L1 and L2 regulations - called SR-L12. Experiments on well known KTH action dataset show that SR-L12 is much better than that of nearest neighbor (NN), nearest subspace (NS), full-space (NF), SRC and collaborative representation classification (CRC). Moreover, the proposed method is comparable to most of state-of-the-art algorithms for human action recognition.