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Spatiotemporal Similarity Search in 3D Motion Capture Gesture Streams

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Advances in Spatial and Temporal Databases (SSTD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9239))

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Abstract

The question of how to model spatiotemporal similarity between gestures arising in 3D motion capture data streams is of major significance in currently ongoing research in the domain of human communication. While qualitative perceptual analyses of co-speech gestures, which are manual gestures emerging spontaneously and unconsciously during face-to-face conversation, are feasible in a small-to-moderate scale, these analyses are inapplicable to larger scenarios due to the lack of efficient query processing techniques for spatiotemporal similarity search. In order to support qualitative analyses of co-speech gestures, we propose and investigate a simple yet effective distance-based similarity model that leverages the spatial and temporal characteristics of co-speech gestures and enables similarity search in 3D motion capture data streams in a query-by-example manner. Experiments on real conversational 3D motion capture data evidence the appropriateness of the proposal in terms of accuracy and efficiency.

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References

  1. Arici, T., Celebi, S., Aydin, A.S., Temiz, T.T.: Robust gesture recognition using feature pre-processing and weighted dynamic time warping. Multimedia Tools Appl. 72(3), 3045–3062 (2014)

    Article  Google Scholar 

  2. Beecks, C.: Distance-based similarity models for content-based multimedia retrieval. PhD thesis, RWTH Aachen University (2013)

    Google Scholar 

  3. Beecks, C., Kirchhoff, S., Seidl, T.: On stability of signature-based similarity measures for content-based image retrieval. Multimedia Tools Appl. 71(1), 349–362 (2014). doi:10.1007/s11042-012-1334-3

    Article  Google Scholar 

  4. Beecks, C., Kirchhoff, S., Seidl, T.: Signature matching distance for content-based image retrieval. In: Proceedings of the ACM International Conference on Multimedia Retrieval, pp. 41–48 (2013)

    Google Scholar 

  5. Beecks, C., Uysal, M.S., Seidl, T.: A comparative study of similarity measures for content-based multimedia retrieval. In: Proceedings of the IEEE International Conference on Multimedia and Expo, pp. 1552–1557 (2010)

    Google Scholar 

  6. Beecks, C., Uysal, M.S., Seidl, T.: Signature quadratic form distance. In: Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 438–445 (2010)

    Google Scholar 

  7. Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: AAAI 1994 workshop on knowledge discovery in databases, pp. 359–370 (1994)

    Google Scholar 

  8. Blackburn, J., Ribeiro, E.: Human motion recognition using isomap and dynamic time warping. In: Elgammal, A., Rosenhahn, B., Klette, R. (eds.) Human Motion 2007. LNCS, vol. 4814, pp. 285–298. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Bodiroža, S., Doisy, G., Hafner, V.V.: Position-invariant, real-time gesture recognition based on dynamic time warping. In: Proceedings of the International Conference on Human-robot Interaction, pp. 87–88 (2013)

    Google Scholar 

  10. Campbell, L.W.: Visual Classification of Co-verbal Gestures for Gesture Understanding. PhD thesis (2001)

    Google Scholar 

  11. Chen, L., Ng, R.: On the marriage of Lp-norms and edit distance. In: Proceedings of the International Conference on Very Large Data Bases, pp. 792–803 (2004)

    Google Scholar 

  12. Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 491–502 (2005)

    Google Scholar 

  13. Cheng, J., Xie, C., Bian, W., Tao, D.: Feature fusion for 3D hand gesture recognition by learning a shared hidden space. Pattern Recogn. Lett. 33(4), 476–484 (2012)

    Article  Google Scholar 

  14. Cienki, A.: Cognitive linguistics: Spoken language and gesture as expressions of conceptualization. Body - Language - Communication: An International Handbook on Multimodality in Human Interaction, pp. 182–201 (2013)

    Google Scholar 

  15. Deza, M., Deza, E.: Encyclopedia of Distances. Springer, Heidelberg (2009)

    Book  MATH  Google Scholar 

  16. Efron, D.: Gesture and Environment. Kings Crown Press, New York (1941)

    Google Scholar 

  17. Ekman, P., Friesen, W.: The repertoire of nonverbal behavior: Categories, origins, usage, and coding. Semiotica 1(1), 49–98 (1969)

    Article  Google Scholar 

  18. Fang, S., Chan, H.: Human identification by quantifying similarity and dissimilarity in electrocardiogram phase space. Pattern Recogn. 42(9), 1824–1831 (2009)

    Article  Google Scholar 

  19. Hahn, M., Krüger, L., Wöhler, C.: 3D action recognition and long-term prediction of human motion. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 23–32. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  20. Hasan, H., Abdul-Kareem, S.: Static hand gesture recognition using neural networks. Artif. Intell. Rev. 41(2), 147–181 (2014)

    Article  Google Scholar 

  21. Hassani, M., Beecks, C., Töws, D., Serbina, T., Haberstroh, M., Niemietz, P., Jeschke, S., Neumann, S., Seidl, T.: Sequential pattern mining of multimodal streams in the humanities. In: Proceedings of the Conference on Database Systems for Business, Technology, and Web, pp. 683–686 (2015)

    Google Scholar 

  22. Hassani, M., Seidl, T.: Towards a mobile health context prediction: Sequential pattern mining in multiple streams. In: Proceedings of the IEEE International Conference on Mobile Data Management, pp. 55–57 (2011)

    Google Scholar 

  23. Hausdorff, F.: Grundzüge der Mengenlehre. Von Veit (1914)

    Google Scholar 

  24. Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.: Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993)

    Article  Google Scholar 

  25. Ibraheem, N.A., Khan, R.Z.: Article: survey on various gesture recognition technologies and techniques. Int. J. Comput. Appl. 50(7), 38–44 (2012)

    Google Scholar 

  26. Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Signal Process. 23(1), 67–72 (1975)

    Article  Google Scholar 

  27. Kendon, A.: Some relationships between body motion and speech. Stud. Dyadic Commun. 7, 177 (1972)

    Article  Google Scholar 

  28. Kendon, A.: Gesticulation and speech: two aspects of the process of utterance. The Relat. Verbal Nonverbal Commun. 25, 207–227 (1980)

    Google Scholar 

  29. Kendon, A.: Gesture: Visible action as utterance. Cambridge University Press (2004)

    Google Scholar 

  30. Keogh, E.J.: Exact indexing of dynamic time warping. In: Proceedings of the International Conference on Very Large Data Bases, pp. 406–417 (2002)

    Google Scholar 

  31. Keskin, C., Erkan, A., Akarun, L.: Real time hand tracking and 3d gesture recognition for interactive interfaces using hmm. ICANN/ICONIPP 26–29, 2003 (2003)

    Google Scholar 

  32. Khan, R.Z., Ibraheem, N.A.: Survey on gesture recognition for hand image postures. pp. 110–121 (2012)

    Google Scholar 

  33. Latecki, L.J., Megalooikonomou, V., Wang, Q., Lakaemper, R., Ratanamahatana, C.A., Keogh, E.: Elastic partial matching of time series. In: European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 577–584 (2005)

    Google Scholar 

  34. LaViola, J.: A survey of hand posture and gesture recognition techniques and technology. Brown University, Providence, RI (1999)

    Google Scholar 

  35. Liu, J., Kavakli, M.: A survey of speech-hand gesture recognition for the development of multimodal interfaces in computer games. In: Proceedings of the IEEE International Conference on Multimedia and Expo, pp. 1564–1569 (2010)

    Google Scholar 

  36. McNeill, D.: Hand and mind: What gestures reveal about thought. University of Chicago Press (1992)

    Google Scholar 

  37. Mitra, S., Acharya, T.: Gesture recognition: a survey. Trans. Sys. Man Cyber Part C 37(3), 311–324 (2007)

    Article  Google Scholar 

  38. Mittelberg, I.: Geometric and image-schematic patterns in gesture space. Equinox Publishing, pp. 351–388 (2010)

    Google Scholar 

  39. Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81(3), 231–268 (2001)

    Article  MATH  Google Scholar 

  40. Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104(2), 90–126 (2006)

    Article  Google Scholar 

  41. Müller, C.: Redebegleitende Gesten. Berliner Wissenschafts-Verlag, Kulturgeschichte - Theorie - Sprachvergleich (1998)

    Google Scholar 

  42. Müller, C., Cienki, A., Fricke, E., Ladewig, S.H., McNeill, D., Teßendorf, S.: Body - Language - Communication: An International Handbook on Multimodality in Human Interaction. (Handbooks of Linguistics and Communication Science 38). De Gruyter Mouton, Berlin/ Boston (2013)

    Google Scholar 

  43. Müller, C., Posner, R.: The Semantics and Pragmatics of Everyday Gestures. Kultur. Weidler, Körper, Zeichen (2004)

    Google Scholar 

  44. Nam, Y., Wohn, K.: Recognition of hand gestures with 3D, nonlinear arm movement. Pattern Recogn. Lett. 18(1), 105–113 (1997)

    Article  Google Scholar 

  45. Park, B.G., Lee, K.M., Lee, S.U.: Color-based image retrieval using perceptually modified hausdorff distance. EURASIP J. Image Video Process. 2008, 4:1–4:10 (2008)

    Google Scholar 

  46. Psarrou, A., Gong, S., Walter, M.: Recognition of human gestures and behaviour based on motion trajectories. Image Vis. Comput. 20(5), 349–358 (2002)

    Article  Google Scholar 

  47. Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43(1), 1–54 (2015)

    Article  Google Scholar 

  48. Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vision 40(2), 99–121 (2000)

    Article  MATH  Google Scholar 

  49. Ruffieux, S., Lalanne, D., Mugellini, E., Abou Khaled, O.: A survey of datasets for human gesture recognition. In: Kurosu, M. (ed.) HCI 2014, Part II. LNCS, vol. 8511, pp. 337–348. Springer, Heidelberg (2014)

    Google Scholar 

  50. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)

    Article  MATH  Google Scholar 

  51. Stern, H., Shmueli, M., Berman, S.: Most discriminating segment-longest common subsequence (MDSLCS) algorithm for dynamic hand gesture classification. Pattern Recogn. Lett. 34(15), 1980–1989 (2013)

    Article  MATH  Google Scholar 

  52. Suk, H.-I., Sin, B.-K., Lee, S.-W.: Recognizing hand gestures using dynamic bayesian network. In: Proceedings of the IEEE International Conference on Automatic Face & Gesture Recognition, pp. 1–6 (2008)

    Google Scholar 

  53. Suk, H.-I., Sin, B.-K., Lee, S.-W.: Hand gesture recognition based on dynamic Bayesian network framework. Pattern Recogn. 43(9), 3059–3072 (2010)

    Article  MATH  Google Scholar 

  54. Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multi-dimensional time-series with support for multiple distance measures. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 216–225 (2003)

    Google Scholar 

  55. Vlachos, M., Kollios, G., Gunopulos, D.: Elastic translation invariant matching of trajectories. Mach. Learn. 58(2–3), 301–334 (2005)

    Article  MATH  Google Scholar 

  56. Watson, R.: A survey of gesture recognition techniques. Technical report,Trinity College Dublin, Department of Computer Science (1993)

    Google Scholar 

  57. Wu, Y., Huang, T.S.: Vision-based gesture recognition: a review. In: Braffort, A., Gibet, S., Teil, D., Gherbi, R., Richardson, J. (eds.) GW 1999. LNCS (LNAI), vol. 1739, pp. 103–115. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  58. Yang, J., Li, Y., Wang, K.: A new descriptor for 3D trajectory recognition via modified CDTW. In: Proceedings of the IEEE International Conference on Automation and Logistics, pp. 37–42 (2010)

    Google Scholar 

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Acknowledgment

This work is partially funded by the Excellence Initiative of the German federal and state governments and by DFG grant SE 1039/7-1.

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Authors and Affiliations

  1. Data Management and Exploration Group, RWTH Aachen University, Aachen, Germany

    Christian Beecks, Marwan Hassani & Thomas Seidl

  2. Department of Linguistics, University of Alberta, Alberta, Canada

    Jennifer Hinnell

  3. Natural Media Lab, RWTH Aachen University, Aachen, Germany

    Daniel Schüller, Bela Brenger & Irene Mittelberg

Authors
  1. Christian Beecks
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  2. Marwan Hassani
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  4. Daniel Schüller
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  5. Bela Brenger
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  6. Irene Mittelberg
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  7. Thomas Seidl
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Corresponding author

Correspondence to Christian Beecks .

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Editors and Affiliations

  1. Naval Academy Research Institute, Brest naval, France

    Christophe Claramunt

  2. University of Florida, Gainesville, Florida, USA

    Markus Schneider

  3. Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Raymond Chi-Wing Wong

  4. Emory University, Atlanta, USA

    Li Xiong

  5. Gachon University, Gyeonggi-do, Korea (Republic of)

    Woong-Kee Loh

  6. University of Southern California, Los Angeles, USA

    Cyrus Shahabi

  7. Department of Computer Science, Pusan National University, Pusan, Korea (Republic of)

    Ki-Joune Li

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Beecks, C. et al. (2015). Spatiotemporal Similarity Search in 3D Motion Capture Gesture Streams. In: Claramunt, C., et al. Advances in Spatial and Temporal Databases. SSTD 2015. Lecture Notes in Computer Science(), vol 9239. Springer, Cham. https://doi.org/10.1007/978-3-319-22363-6_19

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  • DOI: https://doi.org/10.1007/978-3-319-22363-6_19

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