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Inventions 2019, 4(1), 9; https://doi.org/10.3390/inventions4010009

Skeleton-Based Human Action Recognition through Third-Order Tensor Representation and Spatio-Temporal Analysis

1
Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2AZ London, UK
2
RISA Sicherheitsanalysen, GmbH, 10707 Berlin, Germany
*
Author to whom correspondence should be addressed.
Received: 17 December 2018 / Revised: 1 February 2019 / Accepted: 3 February 2019 / Published: 8 February 2019
(This article belongs to the Special Issue Innovation in Machine Intelligence for Critical Infrastructures)
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Abstract

Given the broad range of applications from video surveillance to human–computer interaction, human action learning and recognition analysis based on 3D skeleton data are currently a popular area of research. In this paper, we propose a method for action recognition using depth sensors and representing the skeleton time series sequences as higher-order sparse structure tensors to exploit the dependencies among skeleton joints and to overcome the limitations of methods that use joint coordinates as input signals. To this end, we estimate their decompositions based on randomized subspace iteration that enables the computation of singular values and vectors of large sparse matrices with high accuracy. Specifically, we attempt to extract different feature representations containing spatio-temporal complementary information and extracting the mode-n singular values with regards to the correlations of skeleton joints. Then, the extracted features are combined using discriminant correlation analysis, and a neural network is used to recognize the action patterns. The experimental results presented use three widely used action datasets and confirm the great potential of the proposed action learning and recognition method. View Full-Text
Keywords: human action recognition; higher-order decomposition; discriminant component analysis; pattern recognition human action recognition; higher-order decomposition; discriminant component analysis; pattern recognition
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Barmpoutis, P.; Stathaki, T.; Camarinopoulos, S. Skeleton-Based Human Action Recognition through Third-Order Tensor Representation and Spatio-Temporal Analysis. Inventions 2019, 4, 9.

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