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• W2079133716 abstract "This paper presents an efficient and novel framework for human action recognition based on representing the motion of human body-joints and the theory of nonlinear dynamical systems. Our work is motivated by the pictorial structures model and advances in human pose estimation. Intuitively, a collective understanding of human joints movements can lead to a better representation and understanding of any human action through quantization in the polar space. We use time-delay embedding on the time series resulting of the evolution of human body-joints variables along time to reconstruct phase portraits. Moreover, we train SVM models for action recognition by comparing the distances between trajectories of human body-joints variables within the reconstructed phase portraits. The proposed framework is evaluated on MSR-Action3D dataset and results compared against several state-of-the-art methods." @default.
• W2079133716 created "2016-06-24" @default.
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• W2079133716 date "2013-07-01" @default.
• W2079133716 modified "2023-09-27" @default.
• W2079133716 title "Action Recognition Based on Motion Representing and Reconstructed Phase Spaces Matching of 3D Joint Positions" @default.
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• W2079133716 doi "https://doi.org/10.4028/www.scientific.net/amm.333-335.675" @default.
• W2079133716 hasPublicationYear "2013" @default.
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