Dance Analysis using Multiple Kinect Sensors

Alexandros Kitsikidis, Kosmas Dimitropoulos, Stella Douka, Nikos Grammalidis


In this paper we present a method for body motion analysis in dance using multiple Kinect sensors. The proposed method applies fusion to combine the skeletal tracking data of multiple sensors in order to solve occlusion and self-occlusion tracking problems and increase the robustness of skeletal tracking. The fused skeletal data is split into five different body parts (torso, left hand, right hand, left leg and right leg), which are then transformed to allow view invariant posture recognition. For each part, a posture vocabulary is generated by performing k-means clustering on a large set of unlabeled postures. Finally, body part postures are combined into body posture sequences and Hidden Conditional Random Fields (HCRF) classifier is used to recognize motion patterns (e.g. dance figures). For the evaluation of the proposed method, Tsamiko dancers are captured using multiple Kinect sensors and experimental results are presented to demonstrate the high recognition accuracy of the proposed method.


  1. Aylward, R., “Sensemble: A Wireless Inertial Sensor System for InteractiveDance and Collective Motion Analysis”, Masters of Science in Media Arts and Sciences, Massachusetts Institute of Technology, 2006
  2. Alexiadis, D., Kelly, P., Daras, P., O'Connor, N., Boubekeur, T., and Moussa, M., Evaluating a dancer's performance using kinect-based skeleton tracking. In Proceedings of the 19th ACM international conference on Multimedia (MM 7811). ACM, New York, NY, USA, pp. 659-662, 2011.
  3. Li, W., Zhang, Z., Liu, Z., “Action Recognition Based on A Bag of 3D Points”, IEEE International Workshop on CVPR for Human Communicative Behavior Analysis (in conjunction with CVPR2010), San Francisco, CA, June, 2010.
  4. Wang, J., Liu, Z., Wu, Y., and Yuan, J., “Mining actionlet ensemble for action recognition with depth cameras,” in CVPR'12, 2012.
  5. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A., “Real-time human pose recognition in parts from single depth images,”, in CVPR, pp. 1297 - 1304, June 2011.
  6. Waithayanon, C. and Aporntewan, C., “A motion classifier for Microsoft Kinect,” in Computer Sciences and Convergence Information Technology (ICCIT), 2011 6th International Conference on, 2011.
  7. ten Holt, G. A., Reinders, MJT., Hendricks, EA., MultiDimensional Dynamic Time Warping for Gesture Recognition. Conference Paper. 2007.
  8. Xia, L., Chen, C.-C., and Aggarwal, J., “View invariant human action recognition using histograms of 3D joints,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, 2012.
  9. Wang, S. Quattoni, A., Morency, L.-P., Demirdjian, D., and Trevor Darrell, Hidden Conditional Random Fields for Gesture Recognition, Proceedings IEEE Conference on Computer Vision and Pattern Recognition, June 2006
  10. Kinect for Windows | Voice, Movement & Gesture Recognition Technology. 2013. [ONLINE] Available at: windows/
  11. Besl, Paul J.; N.D. McKay (1992)."A Method for Registration of 3-D Shapes". IEEE Trans. on Pattern Analysis and Machine Intelligence (Los Alamitos, CA, USA: IEEE Computer Society) 14 (2): 239-256.
  12. Rusu, B., Cousins, S., "3D is here: Point Cloud Library (PCL)," Robotics and Automation (ICRA), 2011 IEEE International Conference on , vol., no., pp.1,4, 9-13 May 2011
  13. Quattoni, A., Collins, M., Darrell, T., Conditional Random Fields for Object Recognition, In Neural Information Processing Systems, 2004.

Paper Citation

in Harvard Style

Kitsikidis A., Dimitropoulos K., Douka S. and Grammalidis N. (2014). Dance Analysis using Multiple Kinect Sensors . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: IAMICH, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 789-795. DOI: 10.5220/0004874007890795

in Bibtex Style

author={Alexandros Kitsikidis and Kosmas Dimitropoulos and Stella Douka and Nikos Grammalidis},
title={Dance Analysis using Multiple Kinect Sensors},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: IAMICH, (VISIGRAPP 2014)},

in EndNote Style

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: IAMICH, (VISIGRAPP 2014)
TI - Dance Analysis using Multiple Kinect Sensors
SN - 978-989-758-004-8
AU - Kitsikidis A.
AU - Dimitropoulos K.
AU - Douka S.
AU - Grammalidis N.
PY - 2014
SP - 789
EP - 795
DO - 10.5220/0004874007890795