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In this paper, we propose a novel method to recognize human actions using 3D human skeleton joint points. First,
we represent a skeleton pose by a feature vector with three descriptors: limb orientation, joint motion orientation
and body part relation. Then, we mine discriminative local basic motions based on the sequences of feature
vectors. These local basic motions contain the discriminative motions of key joints and can well represent human
actions. Experiments conducted on MSR Action3D Dataset and MSR Daily Activity3D Dataset demonstrate
the effectiveness of the proposed algorithm and a superior performance over the state-of-the-art techniques.
Xingyang Cai,Wengang Zhou, andHouqiang Li
"An effective representation for action recognition with human skeleton joints", Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92731R (4 November 2014); https://doi.org/10.1117/12.2073573
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Xingyang Cai, Wengang Zhou, Houqiang Li, "An effective representation for action recognition with human skeleton joints," Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92731R (4 November 2014); https://doi.org/10.1117/12.2073573