Automatic Bharatnatyam Dance Posture Recognition using Depth Cameras.

1. Pooja Venkatesh
MS Research Scholar
IIIT-B
Email: pooja.venkatesh@iiitb.org
Automatic Bharatnatyam Dance Posture Recognition
and Expertise Prediction using Depth Cameras.
SAI Intelligent Systems Conference 2016
21-22 September 2016 | London UK
Dinesh Babu Jayagopi
Assistant Professor
IIIT-B
Email: jdinesh@iiitb.ac.in

2. Index  Agenda
 Conclusion
 Pose Recognition
 Expertise Prediction
 Geometry of the Bhangas
 Ground Truth
 Project Flow
 Pose Recognition Results
 Introduction

3. Agenda
Conclusion
Perform pose recognition for 22 basic postures which form the foundation of
Bharatnatyam.
Finding the origin of these postures based on the geometry of the 4 basic forms called
the Bhangas.
Perform expertise prediction of the dancers based on the recognition results.
 Pose recognition and expertise prediction was performed successfully giving an accuracy of
87.14% and 80.80% respectively.
Pose
recognition
Origin
predictions
Expertise
prediction

4. Introduction
Image Courtesy: Ananya, http://www.sehernow.in/ananya2013htmls/kiran-sandhya.html
Bharatnayam is an ancient Indian Classical Dance form originating from the temples of
South India.
“Natyashastra” which is one of the only source of documents for this dance form was
written by a sage named ‘Bharata’ sometime between 200BC and 200AD.



5. Pose Recognition - Selection of Poses
 The Bhangas are the basic forms of Bharatnatyam acting as the foundation for different poses in
this dance form.

6. Geometry of the Bhangas
The basic theory of Bharatnatyam assumes the
entire body to be a mass which is equally
divided along an imaginary line that passes
through the centre of the body.
 The 22 postures are derived from the 4
Bhangas.
 These 22 postures can either be a pure
Bhanga or a mixture of 2 or more Bhangas.

7. Age Group: 13-18 Yrs
Total Dancers: 25
Female Dancers: 23
Male Dancers: 2
Age Group: 6-8 Yrs
Total Dancer: 4
Age Group: 25-35 Yrs
Total Dancers: 15
60
2
Excellent
18-25 Yrs;
37 Female
Dancers
Satisfactory
09-12 Yrs;
17 Female
Dancers
Good
Age Group: >35 Yrs
Total Dancers: 3
Female Dancers: 2
Male Dancers: 1
19
2
Poor
4
15
Ground Truth Statistics

8. Samabhanga-1
(Upright Position)
Pose 1
Pose 2
Pose 4
Pose 5
Abhanga
(30 Degree Variation)
Pose 19
Pose 3
Atibhanga
(Great Diagonal
Bend)
Pose 22
Pose 11
Samabhanga -2
(Half Knee Bend Position)
Pose 6
Pose 7
Pose 8
Pose 9
Pose 10
Samabhanga – 3
(Full Knee Bend)
Pose 12
Pose 13
Pose 14
Tribhanga
(Triple Bend)
Pose 21
Pose 20
Ground Truth Analysis
Pose 18
Pose 15
Pose 16
Pose 17

9. Project Flow

10. 10 Angles between these joints are
used as features for our
classification.
15 Joints are tracked
Joint Tracking

11. Logistic
Regression
82%
SVM RBF
Kernel
87%
K-NN
84%
SVM Linear
Kernel
55%
SVM
Polynomial
Kernel;
Order2
70%
SVM
Polynomial
Kernel;
Order2
84.4%
Naïve Bayes
69%
 The classification of entire dataset consisting of 22 postures from 102 dancers has given the
following results.
Pose Recognition Results

12. The confusion matrix for 22 poses showed similar patterns which could
be grouped to find the parent Bhanga for a set of poses.
A four class classification problem based on the above grouping gave
an accuracy of 93.48%.
This grouping was then used to represent the origin of each of the
poses from their respective Bhangas and verified using Hamming
Distance.
Classification - With grouping

13. 62
21
15
4
NUMBER OF DANCERS
Excellent
Satisfactory
Good
Poor
Expertise Prediction
 A difference of joint angles between Excellent
and Poor rated dancers are used as an additional
feature for expertise prediction.
 Logistic Regression was used for classification
holding 1/3rd of the data out for testing.

14. Excellent
Poor
Satisfactory
Good
93%
64%
56.18%
Excellent
Poor
93%
Satisfactory
Poor
65%
Good
Excellent
Excellent
70%
Good
Satisfactory
Poor
Poor
95.2%Excellent
Good
Satisfactory
Expertise Prediction

15. A novel problem of posture recognition and expertise prediction for one of the most complex dance
forms has been successfully conducted.
We have estimated the origin of the 22 poses from the 4 basic Bhangas with an accuracy of
93.48 %.
Expertise of a dancer based on age group was predicted with an accuracy of
68.46 % without grouping of labels and an accuracy of 80.80 % with grouping.
Expression recognition and subsequent expertise prediction is being carried out at present.




Conclusion

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