Remo Dance Motion Estimation with Markerless Motion Capture Using The Optical Flow Method

  • Neny Kurniati Electronic Engineering Polytechnic Institute of Surabaya
  • Achmad Basuki Electronic Engineering Polytechnic Institute of Surabaya
  • Dadet Pramadihanto Electronic Engineering Polytechnic Institute of Surabaya


Motion capture has been developed and applied in various fields, one of them is dancing. Remo dance is a dance from East Java that tells the struggle of a prince who fought on the battlefield. Remo dancer does not use body-tight costume. He wears a few costume pieces and accessories, so required a motion detection method that can detect limb motion which does not damage the beauty of the costumes and does not interfere motion of the dancer. The method is Markerless Motion Capture. Limbs motions are partial behavior. This means that all limbs do not move simultaneously, but alternately. It required motion tracking to detect parts of the body moving and where the direction of motion. Optical flow is a method that is suitable for the above conditions. Moving body parts will be detected by the bounding box. A bounding box differential value between frames can determine the direction of the motion and how far the object is moving. The optical flow method is simple and does not require a monochrome background. This method does not use complex feature extraction process so it can be applied to real-time motion capture. Performance of motion detection with optical flow method is determined by the value of the ratio between the area of the blob and the area of the bounding box. Estimate coordinates are not necessarily like original coordinates, but if the chart of estimate motion similar to the chart of the original motion, it means motion estimation it can be said to have the same motion with the original.

Keywords: Motion Capture, Markerless, Remo Dance, Optical Flow


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Machdalati Rizky Cendani. Buku Visual Tari Remo Surabayan Sebagai Media Pendukung Hak Paten Kesenian Khas Surabaya. ITS- paper-22017-3407100048. 2012.

Jacky C.P. Chan, H. Leung, Jeff K.T. Tang, and T. Komura. A Virtual Reality Dance Training System Using Motion Capture Technology. IEEE Transactions on Learning Technologies, Vol. 4, no. 2. 2011.

LaViers and M. Egerstedt. The Ballet Automaton: A Formal Model for Human Motion. IEEE/ACM International Conference on Cyber-Physical Systems - ICCPS, 2011.

N. Magnenat-Thalmann, D. Protopsaltou, and E. Kavakli. Learning How to Dance Using a Web 3D Platform. Proc. Sixth Int'l Conf. Web-Based Learning, pp. 1-12, 2007.

Saboune, J., Charpillet, F. Markerless Human Motion Capture for Gait Analysis. Clinical Orthopaedics and Related Research. 3rd European Medical and Biological Engineering Conference. 2005.

Gary Bradski, Adrian Kaehler. Learning OpenCV - Computer Vision with the OpenCV Library. O’Reilly Media, Inc. Ed. 1. 2008

Richard Szeliski. Computer Vision: Algorithms and Applications. Springer. draft. 2010.

Thomas Brox, Bodo Rosenhahn, Daniel Cremers. Contours, Optic Flow, and Prior Knowledge: Cues for Capturing 3D Human Motion in Videos. Springer. Volume 36. pp 265-293. 2008.

Sophia Bakogianni, Evangelia Kavakli, Vicky Karkou, Maroussa Tsakogianni. Teaching Traditional Dance Using E-learning Tools : Experience from The WebDANCE Project. Proceedings DVD of the 21st World Congress on Dance Research, Athens. 2007.

Magnenat-Thalmann, N., Joslin, C. Learning How to Dance on the Internet. In: Interface Conference. Hamburg. 2000.

Thomas Brox, Bodo Rosenhahn, Daniel Cremers, Hans-Peter Seidel. High Accuracy Optical Flow Serves 3-D Pose Tracking: Exploiting Contour and Flow Based Constraints. Springer. Volume 3952. pp 98- 111. 2006.

Ballan, L., Cortelazzo, G.M. Markerless Motion Capture of Skinned Models in a Four Camera Set-Up Using Optical Flow and Silhouettes. In: 3DPVT. 2008.

B.K.P. Horn and B.G. Schunck. Determining Optical Flow. Artificial Intelligence. vol 17, pp 185–203. 1981

B. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. Proceedings of Imaging Understanding Workshop, pages 121—130. 1981.

Bruce D. Lucas. Generalized Image Matching by the Method of Differences. Doctoral Dissertation. 1984.

Barron, J.L., D.J. Fleet, S.S. Beauchemin, and T.A. Burkitt. Performance of Optical Flow Techniques. CVPR. 1992.

Stefano Ferrari. Image segmentation. Universit`a degli Studi di Milano. 2012.

Swamidoss Sathiakumar, Lalit Kumar Awasthi, Roberts Masillamani, S.S. Sridhar. Advances in Intelligent Systems and Computing. ICICIC Global. 2012.

Rujuta R Mahambare. Converting Grayscale Image to Color Image. International Journal of Computer, Information Technology & Bioinformatics. Volume-1, Issue-4. 2013.

Stanley Baron and David Wood. Rec. 601 - The Origins of The 4:2:2 DTV Standard. Ebu Technical Review. 2005.

Ashok Banerji, Ananda Mohan Ghosh. Multimedia Technologies. Tata McGrawHill. 2010.

Harley R. Myler, Atrhur R. Weeks. The Pocket Handbook of Image Processing Algorithms in C. Prentice Hall. 1993.

How to Cite
Kurniati, N., Basuki, A., & Pramadihanto, D. (2016). Remo Dance Motion Estimation with Markerless Motion Capture Using The Optical Flow Method. EMITTER International Journal of Engineering Technology, 3(1), 1-18.