Human activity recognition from skeleton poses release_cawqgl7psvfo7awuc6vk6i4ptq

by Frederico Belmonte Klein, Angelo Cangelosi

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2019  

Abstract

Human Action Recognition is an important task of Human Robot Interaction as cooperation between robots and humans requires that artificial agents recognise complex cues from the environment. A promising approach is using trained classifiers to recognise human actions through sequences of skeleton poses extracted from images or RGB-D data from a sensor. However, with many different data-sets focused on slightly different sets of actions and different algorithms it is not clear which strategy produces highest accuracy for indoor activities performed in a home environment. This work discussed, tested and compared classic algorithms, namely, support vector machines and k-nearest neighbours, to 2 similar hierarchical neural gas approaches, the growing when required neural gas and the growing neural gas.
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Date   2019-08-20
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arXiv  1908.08928v1
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