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Dance modelling, learning and recognition system of Aceh traditional dance based on hidden Markov model
Nurfitri Anbarsanti1, Ary Prihatmanto, S2.
The whole dance of Likok Pulo are modeled by hidden markov model. Dance
gestures are cast as hidden discrete states and phrase as a sequence of
gestures. For robustness under noisy input of Kinect sensor, an angular
representation of the skeleton is designed. A pose of dance is defined by this
angular skeleton representation which has been quantified based on range of
movement. One unique gesture of dance is defined by sequence of pose and
learned and classified by HMM model. Six of dance's gesture classes from the
phrase "Assalamualaikum" has been trained with hundreds of gesture instances
recorded by the Kinect sensor which performed by three of subjects for each
gesture class. The classifier system classify the input testing gesture into one of six
classes of predefined gesture or one class of undefined gesture. The classifier
system has an accuracy of 94.87% for single gesture.
Affiliation:
- Institut Teknologi Bandung, Indonesia
- Institut Teknologi Bandung, Indonesia
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Indexation |
Indexed by |
MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Scopus 2020 |
Impact Factor
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CiteScore (1.4) |
Rank |
Q3 (Engineering (all)) |
Additional Information |
SJR (0.191) |
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