![](bg6.png)
codebook was created with a codebook of k=20
basic postures for each body part using the training
motion sequences. Then, we trained an HCRF using
the train sequences to be able to distinguish between
the three basic Tsamiko dance moves. CRFs with a
varying number of hidden states were trained as can
be seen from Table 1, in which the dance move
detection accuracies of the test set are presented, per
dancer and overall. The best overall detection
accuracy that was achieved is 93,9% using an HCRF
with 11 hidden states. In Table 2, detection
accuracies are presented for each dance move.
Table 1: Recognition accuracies of Tsamiko dance moves
per person and overall recognition accuracies for varying
number of hidden states in the HCRF classifier.
Hidden
States
5 8 11 12 15 20
Dancer A
38,4 61,5 84,6 76,9 76,9 69,2
Dancer B 90,9 90,9 100 100 90,9 72,7
Dancer C 66,6 88,8 100 100 100 77,7
Overall 63,6 78,7
93,9
90,9 87,8 72,7
Table 2: Recognition accuracies of Tsamiko dance moves
for varying number of hidden states in the HCRF
classifier.
Hidden
States
5 8 11 12 15 20
Dance
move 1
83,3 66,6 91,6 100 83,8 100
Dance
move 2
27,2 81,8 90,9 81,8 90,9 36,3
Dance
move 3
80 90 100 90 90 80
Overall 63,6 78,7
93,9
90,9 87,8 72,7
5 CONCLUSIONS AND FUTURE
WORK
This paper presents a study on recognizing
predefined dance motion patterns from skeletal
animation data captured by multiple Kinect sensors.
As can be seen from the experimental results, our
method gave quite promising results providing high
recognition accuracies of the three Tsamiko dance
moves. In future work we aim to experiment on
recognition of different styles of these dance moves
and adding more complex dance patterns and
variations. In addition we plan to extend our
skeleton fusion algorithm on joint rotation data (both
absolute and hierarchical) which will allow the
construction of posture codebooks based on both
position and rotation data.
ACKNOWLEDGEMENTS
The research leading to these results has received
funding from the European Community's Seventh
Framework Programme (FP7-ICT-2011-9) under
grant agreement no FP7-ICT-600676 ''i-Treasures:
Intangible Treasures - Capturing the Intangible
Cultural Heritage and Learning the Rare Know-How
of Living Human Treasures''.
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