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datasets, for which skeleton stream is provided. The
main result is that the descriptor based on bag-of-
gestures outperforms three very recent methods of the
state-of-the-art on the two databases : 88.9% of F
0.5
measure on CUHA database and 84.5% of correct
classification on TUM dataset.
We also study the proposed algorithm on a more
difficult database not designed to extract skeleton: the
public RGBD-HuDaAct database. During the skele-
ton estimation performed with NITE software, some
problems occurred with side or back views of peo-
ple, or when the person has very specific postures that
do not allow skeleton extraction. Even if the RGBD-
HuDaAct database is really challenging to perform
action recognition based on skeletons, it has been con-
sidered here to represent some conditions of video
surveillance system at home. The main result of these
tests if that evenin these conditions, and with a signif-
icant amount of missing data, our descriptor achieves
the state-of-the-art performance of 82% of recogni-
tion rate. To our knowledge, it is the first time that
failures in skeleton extraction are considered during
action recognition.
In future works, we will continue to explore the
links between high-level human actions and elemen-
tary gestures and design a framework to learn middle-
semantic gestures which are the most relevant for hu-
man action recognition. Such framework will allow
us to recognize an action from a small number of
middle-semantic gestures whereas the algorithm pre-
sented in this work recognizes actions from the set of
all gestures. Hence, such framework is likely to both
speed up and increase accuracy of the system. In addi-
tion, we will extend current bag-of-gestures descrip-
tor by taking into account the co-occurrence relation
of different articulations and co-occurrencerelation of
pairs of successive gestures of a given articulation.
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