DANCE MODELLING, LEARNING AND RECOGNITION SYSTEM OF ACEH TRADITIONAL DANCE BASED ON HIDDEN MARKOV MODEL
DOI:
https://doi.org/10.11113/jt.v78.6931Keywords:
Angular skeletal representation, Kinect, dance modelling, dance recognition, gesture recognition, hidden markov model, Likok Pulo danceAbstract
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.
References
Jahn, D. C. W. 1979. Musculoskeletal Examination–Range Of Motion. The Journal of the CCA. 23(2): 51-60.
Rabiner, L. R. 1989. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE. 77(2): 257-286.
Lee, H. K., Kim, J. H. 1999. An HMM-Based Threshold Model Approach for Gesture Recognition. IEEE Transactions on Pattern Analysis and machine Intelligence. 21(10): 961-973.
Apley, A. G. 2010. Apley’s Sistem of Orthopaedics and Fractures. Ninth edition. London: Hodder Arnold.
LaViers, A. & Egerstedt, M. 2011. The Ballet Automaton : A Formal Model for Human Motion. American Control Conference (ACC), 2011. San Francisco, CA, USA. 29 Jun-01 Jul 2011. 3837-3842.
LaViers, A., Chen, Y., Belta, C., Egerstedt, M. 2011. Automatic Generation of Balletic Motions. Proceedings of the 2011 IEEE/ACM Second International Conference on Cyber-Physical Systems. Chicago, IL, USA. 12-14 April 2011. 13-21.
Raptis, M., Kirovski, D., Hoppe, H. 2011. Real-Time Classification of Dance Gestures from Skeleton Animation. ACM SIGGRAPH Symposium on Computer Animation. Vancouver, BC, Canada. 5-6 August 2011.
Hall, J.C. 2011. How to Do Gesture Recognition with Kinect Using Hidden Markov Models (HMMs). [Online]. From : http://www.creativedistraction.com/demos/gesture-recognition-kinect-with-hidden-markov-models-hmms/ [Accessed on 12 March 2014].
Gowing, M., Concolato, C., Izquierdo, E. 2011. Enhanced Visualisation of Dance Performance from Automatically Synchronised Multimodal Recordings. Proceedings of the 19th ACM international conference on Multimedia. New York, USA. November 28-December 01, 2011. 667-670.
Heryadi, Y., Fanany, M. I., Arymurthy, A. M. 2012. A Syntactical Modeling and Classification for Performance Evaluation of Bali Traditional Dance. International Conference on Advanced Computer Science and Informations (ICACSIS). Indonesia. 1-2 December 2012.
Huang, J., Lee, C., Ma, J. 2012. Gesture Recognition and Classification using the Microsoft Kinect. Stanford: Stanford University.
Zhang, H., Du, W. X., Li, H. 2012. Kinect Gesture Recognition for Interactive System. Stanford: Stanford University.
Masurelle, A., Essid, S., Richard, G. 2013. Multimodal Classification of Dance Movements using Body Joint Trajectories and Step Sounds. 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS). Paris, France. 3-5 July 2013. 1-4.
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