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Skeleton Extraction of Dance Sequences from 3D Points Using Convolutional Neural Networks Based on a New Developed C3D Visualization Interface

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The Challenges of the Digital Transformation in Education (ICL 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 917))

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Abstract

A combined approach, involving 3D spatial datasets, noise removal prepossessing and deep learning regression approaches for the estimation of rough skeleton data, is presented in this paper. The application scenario involved data sequences from Greek traditional dances. In particular, a visualization application interface was developed allowing the user to load the C3D sequences, edit the data and remove possible noise. The interface was developed using the OpenGL language and is able to parse aby C3D format file. The interface is supported by several functionalities such as a pre-processing of the 3D point data and noise removal of 3D points that fall apart from the human skeleton. The main research innovation of this paper is the use of a deep machine learning framework through which human skeleton can be extracted. The points are selected on the use of a Convolutional Neural Network (CNN) model. Experimental results on real-life dances being captured by the Vicon motion capturing system are presented to show the great performance of the proposed scheme.

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Notes

  1. 1.

    The developed user interface can be found in https://github.com/JohnCrabs/Crabs3Dv122.

References

  1. Barbara, S.-Y., Shay, A.: The Oxford Handbook of Dance and Ethnicity. Oxford University Press, Oxford (2016)

    Google Scholar 

  2. Dimitropoulos, K., Manitsaris, S., Tsalakanidou, F., Denby, B., Buchman, L., Dupont, S., Nikolopoulos, S., Kompatsiaris, Y., Charisis, V., Hadjileontiadis, L., Pozzi, F., Cotescu, M., Ciftci, S., Katos, A., Manitsaris, A., Grammalidis, N.: A multimodal approach for the safeguarding and transmission of intangible cultural heritage: the case of i-treasures. IEEE Intell. Syst. 1–1. https://doi.org/10.1109/MIS.2018.111144858 (2018)

    Article  Google Scholar 

  3. Doulamis, A.D., Voulodimos, A., Doulamis, N.D., Soile, S., Lampropoulos, A.: Transforming intangible folkloric performing arts into tangible choreographic digital objects: the terpsichore approach. In: International Conference on Computer Vision, Theory and Applications (VISIGRAPP), Porto, Portugal, pp. 451–460 (2017)

    Google Scholar 

  4. Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimed. 19, 4–10 (2012)

    Article  Google Scholar 

  5. Windolf, M., Gtzen, N., Morlock, M.: Systematic accuracy and precision analysis of video motion capturing systems-exemplified on the Vicon-460 system. J. Biomech. 41, 2776–2780 (2008)

    Article  Google Scholar 

  6. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images, pp. 1297–1304 (2011)

    Google Scholar 

  7. Kitsikidis, A., Dimitropoulos, K., Douka, S., Grammalidis, N.: Dance analysis using multiple kinect sensors. In: VISAPP 2014—Proceedings of the 9th International Conference on Computer Vision Theory and Applications, vol. 2, pp. 789–795 (2014)

    Google Scholar 

  8. Kim, D., Kim, D.-H., Kwak, K.-C.: Classification of k-pop dance movements based on skeleton information obtained by a kinect sensor. Sensors 17, 1261 (2017)

    Article  Google Scholar 

  9. Hisatomi, K., Katayama, M., Tomiyama, K., Iwadate, Y.: 3D archive system for traditional performing arts: application of 3D reconstruction method using graph-cuts. Int. J. Comput. Vis. 94, 78–88 (2011)

    Article  Google Scholar 

  10. Stavrakis, E., Aristidou, A., Savva, M., Himona, S., Chrysanthou, Y.: Digitization of cypriot folk dances. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7616, pp. 404–413 (2012)

    Chapter  Google Scholar 

  11. Rallis, I., Georgoulas, I., Doulamis, N., Voulodimos, A., Terzopoulos, P.: Extraction of key postures from 3D human motion data for choreography summarization. In: Proceedings of the IEEE 9th International Conference on Virtual Worlds and Games for Serious Applications, (VS-Games), pp. 94–101 (2017)

    Google Scholar 

  12. Rallis, I., Doulamis, N., Doulamis, A., Voulodimos, A., Vescoukis, V.: Spatio-temporal summarization of dance choreographies. Comput. Graph. 73, 88–101 (2018)

    Article  Google Scholar 

  13. Elhamifar, E., Sapiro, G., Vidal, R.: See all by looking at a few: sparse modeling for finding representative objects. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1600–1607 (2012)

    Google Scholar 

  14. Wang, H.: A survey on deep neural networks for human action recognition based on skeleton information. In: Advances in Intelligent Systems and Computing, vol. 541, pp. 329–336 (2017)

    Google Scholar 

  15. Protopapadakis, E., Voulodimos, A., Doulamis, A., Camarinopoulos, S., Doulamis, N., Miaoulis, G.: Dance pose identification from motion capture data: a comparison of classifiers. Technologies 6(1), 31 (2018)

    Article  Google Scholar 

  16. Dewan, S., Agarwal, S., Singh, N.: A deep learning pipeline for Indian dance style classification, vol. 10696 (2018)

    Google Scholar 

  17. Dimitropoulos, K., Barmpoutis, P., Kitsikidis, A., Grammalidis, N.: Classification of multidimensional time-evolving data using histograms of Grassmannian points. IEEE Trans. Circuits Syst. Video Technol. 28, 892–905 (2018)

    Article  Google Scholar 

  18. Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 2018, 13 pages (2018)

    Google Scholar 

  19. McReynolds, T., Blythe, D.: Advanced Graphics Programming Using OpenGL. Morgan Kaufmann Publishers Inc., San Francisco (2005)

    Google Scholar 

  20. Motion Lab systems Inc.: The C3D File Format User Guide. United States of America, 1997–2008

    Google Scholar 

  21. Alfalah, S., Chan, W., Khan, S., Falah, J., Alfalah, T., Harrison, D., Charissis, V.: Gait analysis data visualisation in virtual environment (GADV/VE). In: Proceedings of 2014 Science and Information Conference, SAI 2014, pp. 742–751 (2014)

    Google Scholar 

  22. Barre, A., Armand, S.: Biomechanical toolkit: open-source framework to visualize and process biomechanical data. Comput. Methods Programs Biomed. 114(1), 80–87 (2014)

    Article  Google Scholar 

  23. Nguyen, T.-H., Huynh, V.-N.: A k-means-like algorithm for clustering categorical data using an information theoretic-based dissimilarity measure. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9616, pp. 115–130 (2016)

    Google Scholar 

  24. Makantasis, K., Doulamis, A., Doulamis, N., Psychas, K.: Deep learning based human behavior recognition in industrial workflows. In: Proceedings—International Conference on Image Processing, ICIP, August 2016, pp. 1609–1613 (2016)

    Google Scholar 

  25. Laptev, I., Oquab, M., Bottou, L., Sivic, J.: Is object localization for free? - weakly-supervised learning with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, pp. 685–694, June 2011

    Google Scholar 

  26. Jia, Y., Szegedy, C., Liu, W.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, Mass, USA. p. 19, June 2011

    Google Scholar 

  27. Wikipedia. Convolutional neural network. https://en.wikipedia.org/wiki/Convolutional_neural_network

  28. Sutskever, I., Krizhevsky, A., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS 2012), Lake Tahoe, Nev, USA, pp. 1097–1105, December 2012

    Google Scholar 

  29. Darrell, T., Girshick, R., Donahue, J., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587, June 2015

    Google Scholar 

  30. Doulamis, N., Doulamis, A.: Semi-supervised deep learning for object tracking and classification, pp. 848–852 (2014)

    Google Scholar 

  31. Doulamis, N., Doulamis, A.: Fast and adaptive deep fusion learning for detecting visual objects. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7585, no. PART 3, pp. 345–354 (2012)

    Chapter  Google Scholar 

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Acknowledgments

This work was supported by the EU H2020 TERPSICHORE project “Transforming Intangible Folkloric Performing Arts into Tangible Choreographic Digital Objects” under the grant agreement 691218.

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Correspondence to Anastasios Doulamis .

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Kavouras, I., Protopapadakis, E., Doulamis, A., Doulamis, N. (2019). Skeleton Extraction of Dance Sequences from 3D Points Using Convolutional Neural Networks Based on a New Developed C3D Visualization Interface. In: Auer, M., Tsiatsos, T. (eds) The Challenges of the Digital Transformation in Education. ICL 2018. Advances in Intelligent Systems and Computing, vol 917. Springer, Cham. https://doi.org/10.1007/978-3-030-11935-5_26

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