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4 November 2014 An effective representation for action recognition with human skeleton joints
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Abstract
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.
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Xingyang Cai, Wengang Zhou, and 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
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