Human activity recognition from skeleton poses
release_cawqgl7psvfo7awuc6vk6i4ptq
by
Frederico Belmonte Klein, Angelo Cangelosi
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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