Abstract
Performing arts and in particular dance is one of the most important domains of Intangible Cultural Heritage. However, preserving, documenting, analyzing and visually understanding choreographic patterns is a challenging task due to technical difficulties it involves. A choreography is a time-varying 3D process (4D) including dynamic co-interactions among different actors (dancers), emotional and style attributes, as well as supplementary ICH elements such as the music tempo, the rhythm, traditional costumes etc. Recent technological advancements have unleashed tremendous possibilities in capturing, documenting and storing Intangible Cultural Heritage content, which can now be generated at a greater volume and quality than ever before. The massive amounts of RGB-D and 3D skeleton data produced by video and motion capture devices. The huge number of different types of existing dances and variations dictate the need for organizing, archiving and analyzing dance-related cultural content in a tractable fashion and with lower computational and storage resource requirements. Motion capturing devices are programmable to extract humans’ skeleton data in terms of 3D points each corresponding to a human joint. This information can be combined with computer graphics software toolkits for modelling, classification and summarization purposes. In this chapter, we present recent trends in choreographic representation in terms of modelling, summarization and choreographic pose recognition. We survey recent approaches employed for the extraction of representative primitives of choreographic sequences, the recognition of choreographic pose and dance movements, as well as for the analysis and semantic representation of choreographic patterns.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aristidou A, Chrysanthou Y (2013) Motion indexing of different emotional states using LMA components. In: SIGGRAPH Asia 2013 technical briefs, New York, NY, USA, pp 21:1–21:4
Aristidou P, Stavrakis E, Charalambous P, Chrysanthou Y, Himona SL (2015a) Folk dance evaluation using laban movement analysis. J Comput Cult Herit 8(4):20:1–20:19
Aristidou A, Charalambous P, Chrysanthou Y (2015b) Emotion analysis and classification: understanding the performers’ emotions using the LMA entities. Comput Graphics Forum 34(6):262–276 (2015)
Aristidou A, Stavrakis E, Papaefthimiou M, Papagiannakis G, Chrysanthou Y (2018) Style-based motion analysis for dance composition. Vis Comput 34(12):1725–1737
Bakalos N, Protopapadakis E, Doulamis A, Doulamis N (2018) Dance posture/steps classification using 3D joints from the kinect sensors. In: 2018 IEEE 16th international conference on dependable, autonomic and secure computing, 16th international conference on pervasive intelligence and computing, 4th international conference on big data intelligence and computing and cyber science and technology congress (DASC/PiCom/DataCom/CyberSciTech), pp 868–873
Ballas A, Santad T, Sookhanaphibarn K, Choensawat W (2017) Game-based system for learning labanotation using Microsoft Kinect. In: 2017 IEEE 6th global conference on consumer electronics (GCCE), pp 1–3
Barmpoutis P, Stathaki T, Camarinopoulos S (2019) Skeleton-based human action recognition through third-order tensor representation and spatio-temporal analysis. Inventions 4(1)
Bouchard D, Badler N (2007) Semantic segmentation of motion capture using laban movement analysis. In: Intelligent virtual agents, pp 37–44
Cao Z, Simon T, Wei S-E, Sheikh Y (2016) Realtime multi-person 2D pose estimation using part affinity fields. arXiv:1611.08050 [cs]
Chai J, Hodgins JK (2005) Performance animation from low-dimensional control signals. In: ACM SIGGRAPH 2005 papers. New York, NY, USA, pp 686–696
Chan C, Ginosar S, Zhou T, Efros AA (2018) Everybody dance now. arXiv:1808.07371 [cs]
Chen L, Wei H, Ferryman J (2013) A survey of human motion analysis using depth imagery. Pattern Recogn Lett 34(15):1995–2006
Choensawat W, Nakamura M, Hachimura K (2015) GenLaban: a tool for generating labanotation from motion capture data. Multimed Tools Appl 74(23):10823–10846
Crnkovic-Friis L, Crnkovic-Friis L (2016) Generative choreography using deep learning. arXiv:1605.06921 [cs]
Dewan S, Agarwal S, Singh N (2018) Spatio-temporal laban features for dance style recognition. In: 2018 24th international conference on pattern recognition (ICPR), pp 2911–2916
Dimitropoulos K et al (2014) Capturing the intangible an introduction to the i-Treasures project. In: 2014 international conference on computer vision theory and applications (VISAPP), vol 2, pp 773–781
Doulamis A et al (2013) 4D reconstruction of the past. In: First international conference on remote sensing and geoinformation of the environment (RSCy2013), vol 8795, p 87950
Doulamis N, Doulamis A, Ioannidis C, Klein M, Ioannides M (2017) Modelling of static and moving objects: digitizing tangible and intangible cultural heritage. In: Mixed reality and gamification for cultural heritage, Springer, Cham, pp 567–589
Elhamifar E, Sapiro G, Vidal R (2012) See all by looking at a few: sparse modeling for finding representative objects. In: 2012 IEEE conference on computer vision and pattern recognition, pp 1600–1607
ERIC—ED059225—The mastery of movement 1971, July. https://eric.ed.gov/?id=ED059225. Accessed 11 July 2019
Ferguson S, Schubert E, Stevens CJ (2014) Dynamic dance warping: using dynamic time warping to compare dance movement performed under different conditions. In: Proceedings of the 2014 international workshop on movement and computing, New York, NY, USA, pp 94:94
Hachimura K, Takashina K, Yoshimura M (2005) Analysis and evaluation of dancing movement based on LMA. In: ROMAN 2005. IEEE international workshop on robot and human interactive communication, pp 294–299
Hajdin M, Kico I, Dolezal M, Chmelik J, Doulamis A, Liarokapis F (2019) Digitization and visualization of movements of slovak folk dances. In: The challenges of the digital transformation in education, pp 245–256
Hisatomi K, Katayama M, Tomiyama K, Iwadate Y (2011) 3D archive system for traditional performing arts. Int J Comput Vis 94(1):78–88
Hutchinson A, Hutchinson WA, Guest AH (1970) Labanotation: or, kinetography Laban: the system of analyzing and recording movement. Taylor & Francis
Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, pp 5967–5976
Kahol K, Tripathi P, Panchanathan S (2004) Automated gesture segmentation from dance sequences. In: Proceedings of 2004 sixth IEEE international conference on automatic face and gesture recognition, pp 883–888
Kapadia M, Chiang I, Thomas T, Badler NI, Kider JT Jr (2013) Efficient motion retrieval in large motion databases. In: Proceedings of the ACM SIGGRAPH symposium on interactive 3D graphics and games, New York, NY, USA, pp 19–28
Kavakli E, Bakogianni S, Damianakis A, Loumou M, Tsatsos D (2004) Traditional dance and E-learning: the WebDance learning environment
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: The challenges of the digital transformation in education, pp 267–279
Kico I, Grammalidis N, Christidis Y, Liarokapis F (2018) Digitization and visualization of folk dances in cultural heritage: a review. Inventions 3(4):72
Kim D, Jang M, Yoon Y, Kim J (2015) Classification of dance motions with depth cameras using subsequence dynamic time warping. In: 2015 8th international conference on signal processing, image processing and pattern recognition (SIP), pp 5–8
Kitsikidis A et al (2015) A game-like application for dance learning using a natural human computer interface. In: Universal access in human-computer interaction. Access to Learning, Health and Well-Being, pp 472–482
Kitsikidis A, Dimitropoulos K, Douka S, Grammalidis N (2018) Dance analysis using multiple Kinect sensors. In: 2014 international conference on computer vision theory and applications (VISAPP), vol 2, pp 789–795
Kohn B, Nowakowska A, Belbachir AN (2012) Real-time body motion analysis for dance pattern recognition. In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops, pp 48–53
Kojima K, Hachimura K, Nakamura M (2009) LabanEditor: graphical editor for dance notation. In: 11th IEEE international workshop on robot and human interactive communication proceedings, pp 59–64
Krüger B, Tautges J, Weber A, Zinke A (2010) Fast local and global similarity searches in large motion capture databases. In: Proceedings of the 2010 ACM SIGGRAPH/eurographics symposium on computer animation, Goslar Germany, Germany, pp 1–10
Krüger B, Vögele A, Willig T, Yao A, Klein R, Weber A (2017) Efficient unsupervised temporal segmentation of motion data. IEEE Trans Multimed 19(4):797–812
Lee J, Chai J, Reitsma PSA, Hodgins JK, Pollard NS (2002) Interactive control of avatars animated with human motion data. In: Proceedings of the 29th annual conference on computer graphics and interactive techniques, New York, NY, USA, pp 491–500
Liutkus A, Dremeau A, Alexiadis D, Essid S, Daras P (2012) Analysis of dance movements using gaussian processes: extended abstract. In: Proceedings of the 20th ACM international conference on multimedia, New York, NY, USA, pp 1375–1376
Masurelle A, Essid S, Richard G (2013) Multimodal classification of dance movements using body joint trajectories and step sounds. In: 2013 14th international workshop on image analysis for multimedia interactive services (WIAMIS), pp 1–4
Mehta D et al (2017) Monocular 3D human pose estimation in the wild using improved CNN supervision. In: 2017 international conference on 3D vision (3DV), pp 506–516
Protopapadakis E, Grammatikopoulou A, Doulamis A, Grammalidis N (2017) Folk dance pattern recognition over depth images acquired via kinect sensor. ISPRS-Int Arch Photogramm Remote Sens Spat Inf Sci 587–593
Protopapadakis E, Voulodimos A, Doulamis A, Camarinopoulos S, Doulamis N, Miaoulis G (2018) Dance pose identification from motion capture data: a comparison of classifiers. Technologies 6(1):31
Rallis I, Georgoulas I, Doulamis N, Voulodimos A, Terzopoulos P (2017) Extraction of key postures from 3D human motion data for choreography summarization. In: 2017 9th international conference on virtual worlds and games for serious applications (VS-Games), pp 94–101
Rallis I, Langis A, Georgoulas I, Voulodimos A, Doulamis N, Doulamis A (2018a) An embodied learning game using kinect and labanotation for analysis and visualization of dance kinesiology. In: 2018 10th international conference on virtual worlds and games for serious applications (VS-Games), pp 1–8
Rallis I, Doulamis N, Doulamis A, Voulodimos A, Vescoukis V (2018b) Spatio-temporal summarization of dance choreographies. Comput Graph 73:88–101
Raptis M, Kirovski D, Hoppe H (2011) Real-time classification of dance gestures from skeleton animation. In: Proceedings of the 2011 ACM SIGGRAPH/eurographics symposium on computer animation, New York, NY, USA, pp 147–156
Rizzo A et al (2018) WhoLoDancE: whole-body interaction learning for dance education
Shay A, Sellers-Young B (2016) The Oxford handbook of dance and ethnicity. Oxford University Press
Shiratori T, Nakazawa A, Ikeuchi K (2006) Dancing-to-music character animation. Comput Graph Forum 25(3):449–458
Tang T, Jia J, Mao H (2018) Dance with melody: an LSTM-autoencoder approach to music-oriented dance synthesis. In: Proceedings of the 26th ACM international conference on multimedia, New York, NY, USA, pp 1598–1606
Voulodimos A, Doulamis N, Fritsch D, Makantasis K, Doulamis A, Klein M (2016) Four-dimensional reconstruction of cultural heritage sites based on photogrammetry and clustering. J Electron Imaging 26:011013
Voulodimos A, Rallis I, Doulamis N (2018a) Physics-based keyframe selection for human motion summarization. Multimed Tools Appl
Voulodimos A, Doulamis N, Doulamis A, Rallis I (2018b) Kinematics-based extraction of salient 3D human motion data for summarization of choreographic sequences. In: 2018 24th international conference on pattern recognition (ICPR), pp 3013–3018
Wang J, Miao Z, Guo H, Zhou Z, Wu H (2017) Using automatic generation of Labanotation to protect folk dance. JEI 26(1):011028
Wang T-C, Liu M-Y, Zhu J-Y, Tao A, Kautz J, Catanzaro B (2018) High-resolution image synthesis and semantic manipulation with conditional GANs. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp 8798–8807
Wolf W (1996) Key frame selection by motion analysis. In: 1996 IEEE international conference on acoustics, speech, and signal processing conference proceedings, vol 2, pp 1228–1231
Zacharatos H, Gatzoulis C, Chrysanthou Y, Aristidou A (2013) Emotion recognition for exergames using laban movement analysis. In: Proceedings of motion on games, New York, NY, USA, pp 39:61–39:66
Zhang Z (2012) Microsoft kinect sensor and its effect. IEEE Multimed 19(2):4–10
Zhou F, Torre FD, Hodgins JK (2013) Hierarchical aligned cluster analysis for temporal clustering of human motion. IEEE Trans Pattern Anal Mach Intell 35(3):582–596
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rallis, I., Voulodimos, A., Bakalos, N., Protopapadakis, E., Doulamis, N., Doulamis, A. (2020). Machine Learning for Intangible Cultural Heritage: A Review of Techniques on Dance Analysis. In: Liarokapis, F., Voulodimos, A., Doulamis, N., Doulamis, A. (eds) Visual Computing for Cultural Heritage. Springer Series on Cultural Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-37191-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-37191-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37190-6
Online ISBN: 978-3-030-37191-3
eBook Packages: Computer ScienceComputer Science (R0)