PoGest: A vision based tool for facilitating Kathak learning

We present a vision based tool for enriching the conventional method of learning Kathak. Teachers can record their dance steps using PoGest and give it to the learners for practice according to their pace and convenience. PoGest will help learners to analyze their dance movements by giving them immediate feedback in the form of similarity score and also the concurrent display of juxtaposed recordings of their own performance with respect to that of the teacher's.

[1]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[2]  Taku Komura,et al.  A Virtual Reality Dance Training System Using Motion Capture Technology , 2011, IEEE Transactions on Learning Technologies.

[3]  Rayi Yanu Tara,et al.  Hand Segmentation from Depth Image using Anthropometric Approach in Natural Interface Development , 2012 .

[4]  Yang Yang,et al.  Automatic Dance Lesson Generation , 2012, IEEE Transactions on Learning Technologies.

[5]  Rwitajit Majumdar,et al.  Framework for Teaching Bharatanatyam through Digital Medium , 2012, 2012 IEEE Fourth International Conference on Technology for Education.

[6]  Junsong Yuan,et al.  Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera , 2011, ACM Multimedia.

[7]  Henning Pohl,et al.  Dance Pattern Recognition using Dynamic Time Warping , 2010 .

[8]  Nikolaos G. Bourbakis,et al.  A survey of skin-color modeling and detection methods , 2007, Pattern Recognit..

[9]  Philip Chan,et al.  Toward accurate dynamic time warping in linear time and space , 2007, Intell. Data Anal..

[10]  Darko Kirovski,et al.  Real-time classification of dance gestures from skeleton animation , 2011, SCA '11.

[11]  Javid Taheri,et al.  SparseDTW: A Novel Approach to Speed up Dynamic Time Warping , 2009, AusDM.

[12]  Janusz Konrad,et al.  A gesture-driven computer interface using Kinect , 2012, 2012 IEEE Southwest Symposium on Image Analysis and Interpretation.

[13]  Chung-Lin Huang,et al.  Hand gesture recognition using a real-time tracking method and hidden Markov models , 2003, Image Vis. Comput..

[14]  Markus Koskela,et al.  Sequence Alignment for RGB-D and Motion Capture Skeletons , 2013, ICIAR.