1 June 2018 Human action recognition using depth motion maps pyramid and discriminative collaborative representation classifier
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Abstract
Human action recognition is a challenging task in machine learning and pattern recognition. This paper presents an action recognition framework based on depth sequences. An effective feature descriptor named depth motion maps pyramid (DMMP) inspired by DMMs is developed. First, a series of DMMs with temporal scales are constructed to effectively capture spatial–temporal motion patterns of human actions. Then these DMMs are fused to obtain the final descriptor named DMMs pyramid. Second, we propose a discriminative collaborative representation classifier (DCRC), where an extra constraint on the collaborative coefficient is imposed to provide prior knowledge for the representation coefficient. In addition, we apply DCRC to encode the obtained features and recognize the human actions. The proposed framework is evaluated on MSR three-dimensional (3-D) action datasets, MSR hand gesture dataset, UTD-MHAD, and MSR daily Activity3D dataset, respectively. The experimental results indicate the effectiveness of our proposed method for human action recognition.
© 2018 SPIE and IS&T
Jun Kong, Baofeng Zan, Min Jiang, "Human action recognition using depth motion maps pyramid and discriminative collaborative representation classifier," Journal of Electronic Imaging 27(3), 033027 (1 June 2018). https://doi.org/10.1117/1.JEI.27.3.033027 . Submission: Received: 29 September 2017; Accepted: 11 May 2018
Received: 29 September 2017; Accepted: 11 May 2018; Published: 1 June 2018
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