Semi-automatic annotation and MPEG-7 authoring of dance videos
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Modeling the Dance Video Semantics using Regular Tree Automata
Fundamenta Informaticae
A Dance Synthesis System Using Motion Capture Data
Knowledge Acquisition: Approaches, Algorithms and Applications
DANCING, Dance ANd Choreography: an Intelligent Nondeterministic Generator
The Fifth Richard Tapia Celebration of Diversity in Computing Conference: Intellect, Initiatives, Insight, and Innovations
Using Concept Recognition to Annotate a Video Collection
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Recognizing hand gestures of a dancer
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Nrityakosha: Preserving the intangible heritage of Indian classical dance
Journal on Computing and Cultural Heritage (JOCCH)
Annotating Dance Posture Images Using Multi Kernel Feature Combination
NCVPRIPG '11 Proceedings of the 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics
Human Pose Co-Estimation and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.00 |
BharataNatyam (BN) like any other Indian classical dance comprises of a sequence of possible and legitimate dance steps. It is estimated that using the main body parts namely head, neck, hand and leg itself, more than 5 lakh dance steps can be generated for a single beat. Choreographers and even dancers usually repeat their favorite dance steps or the conventional casual dance steps taught by their teacher while performing for multiple beats. As a result several valid and many other significant non-traditional dance steps remain unexplored. Hence, we propose to have an auto enumeration followed by auto classification of significant BN dance steps that can be used in dance performance and choreography. In short, we try to transform sheer art into a System Modelled art i.e. 'Art to SMart'. The foremost and most challenging task is to have a computational model that represents different BN dance poses. In this paper, we have proposed a computational model to represent BN dance steps and have presented the detailed description of formulation of a dance position vector that comprises of thirty explicitly identified attributes to capture and represent all variations of a BN dance step. We have named it as a SMart system for modelling BN steps, where SMart stands for System Modelled art. We have also demonstrated sample dance steps and their corresponding representations with appropriate dance step images.