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View-Invariant Pose Recognition Using Multilinear Analysis and the Universum

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Advances in Visual Computing (ISVC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5359))

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Abstract

This paper presents an approach to full-body human pose recognition. Inputs to the proposed approach are pairs of silhouette images obtained from wide baseline binocular cameras. Through multilinear analysis, low dimensional view-invariant pose coefficient vectors can be extracted from these stereo silhouette pairs. Taking these pose coefficient vectors as features, the Universum method is trained and used for pose recognition. Experiment results obtained using real image data showed the efficacy of the proposed approach.

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References

  1. Ong, S., Ranganath, S.: Automatic sign language analysis: a survey and the future beyond lexical meaning. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 873–891 (2005)

    Article  Google Scholar 

  2. Sul, C., Lee, K., Wohn, K.: Virtual stage: A location-based karaoke system. IEEE Multimedia 05, 42–52 (1998)

    Article  Google Scholar 

  3. Camurri, A., Hashimoto, S., Ricchetti, M., Ricci, A., Suzuki, K., Trocca, R., Volpe, G.: Eyesweb: Toward gesture and affect recognition in interactive dance and music systems. Computer Music Journal 24, 57–69 (2000)

    Article  Google Scholar 

  4. Qian, G., Guo, F., Ingalls, T., Olson, L., James, J., Rikakis, T.: A gesture-driven multimodal interactive dance system. In: Proceedings of IEEE International Conference on Multimedia and Expo. (2004)

    Google Scholar 

  5. Jenkins, O.C., González, G., Loper, M.M.: Tracking human motion and actions for interactive robots. In: HRI 2007: Proceedings of the ACM/IEEE international conference on Human-robot interaction, pp. 365–372. ACM, New York (2007)

    Google Scholar 

  6. Ng, C.W., Ranganath, S.: Gesture recognition via pose classification. In: Proceedings of 15th International Conference on Pattern Recognition, 2000, vol. 3, pp. 699–704 (2000)

    Google Scholar 

  7. 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, vol. 1739, pp. 103–115. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  8. Cui, Y., Swets, D.L., Weng, J.: Learning-based hand sign recognition using SHOSLIF-m. In: Proceedings of International Conference on Computer Vision, pp. 631–636 (1995)

    Google Scholar 

  9. Wu, Y., Huang, T.: View-independent recognition of hand postures. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 88–94 (2000)

    Google Scholar 

  10. Imai, A., Shimada, N., Shirai, Y.: 3-d hand posture recognition by training contour variation. In: Proceedings of the International Conference on Face and Gesture Recognition, pp. 895–900 (2004)

    Google Scholar 

  11. Singh, M., Mandal, M., Basu, A.: Pose recognition using the radon transform. In: 48th Midwest Symposium on Circuits and Systems, 2005, vol. 2, pp. 1091–1094 (2005)

    Google Scholar 

  12. Haritaoglu, I., Harwood, D., Davis, L.S.: Ghost: A human body part labeling system using silhouettes. In: Proceedings of the IEEE International Conference on Pattern Recognition (1998)

    Google Scholar 

  13. Bradski, G.R., Davis, J.W.: Motion segmentation and pose recognition with motion history gradients. Machine Vision and Applications 13, 174–184 (2002)

    Article  Google Scholar 

  14. Cohen, I., Li, H.: Inference of human postures by classification of 3d human body shape. In: Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (2003)

    Google Scholar 

  15. Cheung, K.M., Baker, S., Kanade, T.: Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture. In: Proc. CVPR, pp. 77–84 (2003)

    Google Scholar 

  16. Mikic, I., Trivedi, M.M., Hunter, E., Cosman, P.C.: Human body model acquisition and tracking using voxel data. International Journal of Computer Vision 53, 199–223 (2003)

    Article  MATH  Google Scholar 

  17. Kakadiaris, I.A., Metaxas, D.: Model-based estimation of 3d human motion with occlusion based on active multi-viewpoint selection. In: Proc. CVPR, pp. 81–87 (1996)

    Google Scholar 

  18. Li, R., Yang, M.H., Sclaro, S., Tian, T.P.: Monocular tracking of 3d human motion with a coordinated mixture of factor analyzers. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 137–150. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  19. Urtasun, D.F.R., Fua, P.: 3d people tracking with gaussian process dynamical models. In: Proc. CVPR, pp. 238–245 (2006)

    Google Scholar 

  20. Elgammal, A., Lee, C.: Inferring 3d body pose from silhouettes using activity manifold learning. In: Proc. CVPR, pp. 681–688 (2004)

    Google Scholar 

  21. Rosales, R., Sclaro, S.: Learning body pose via specialized maps. In: Proc. Conference on Neural Information Processing Systems, pp. 1263–1270 (2002)

    Google Scholar 

  22. Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Computer Vision and Image Understanding 81, 231–268 (2001)

    Article  MATH  Google Scholar 

  23. Moeslund, T.B., Hilton, A., Kruger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104, 90–126 (2006)

    Article  Google Scholar 

  24. Wang, W.L., Tan, T.: Recent development in human motion analysis. Pattern Recognition 36, 585–601 (2003)

    Article  Google Scholar 

  25. Chu, C., Cohen, I.: Pose and gesture recognition using 3d body shapes decomposition. In: Proc. CVPR, pp. 69–78 (2005)

    Google Scholar 

  26. Boulay, B., Bremond, F., Thonnat, M.: Applying 3d human model in a pose recognition system. Pattern Recognition Letters 27, 1788–1796 (2006)

    Article  Google Scholar 

  27. Huang, F., Di, H., Xu, G.-Y.: Viewpoint insensitive posture representation for action recognition. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2006. LNCS, vol. 4069, pp. 143–152. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  28. Guo, F., Qian, G.: Monocular 3d tracking of articulated human motion in silhouette and pose manifolds. EURASIP Journal on Image and Video Processing (2008)

    Google Scholar 

  29. Guo, F., Qian, G.: Dance pose recognition using wide-baseline orthogonal stereo cameras. In: Proc. FGR, pp. 481–486 (2006)

    Google Scholar 

  30. Guo, F.: Robust Visual Tracking of Articulated Human Motion. PhD thesis, Arizona State University (2007)

    Google Scholar 

  31. Peng, B., Qian, G.: Binocular dance pose recognition and body orientation estimation via multilinear analysis. In: Proceedings of Workshop on Tensors in Image Processing and Computer Vision in conjunction with CVPR (2008)

    Google Scholar 

  32. Elden, L.: Matrix Methods in Data Mining and Pattern Recognition. SIAM, Philadelphia (2007)

    Book  MATH  Google Scholar 

  33. Kiers, H.A.L.: An alternating least squares algorithms for parafac2 and three-way dedicom. Computational Statistics & Data Analysis 16, 103–118 (1993)

    Article  MATH  Google Scholar 

  34. Weston, J., Collobert, R., Sinz, F., Bottou, L., Vapnik, V.: Inference with the universum. In: Proceedings of the 23rd International Conference on Machine Learning, p. 127 (2006)

    Google Scholar 

  35. Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research

    Google Scholar 

  36. Chapelle, O., Scholkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2006)

    Book  Google Scholar 

  37. Sinz, F., Chapelle, O., Agarwal, A., Schölkopf, B.: An analysis of inference with the universum. In: Proceedings of the 20th Annual Conference on Neural Information Processing Systems (NIPS), pp. 1–8 (2007)

    Google Scholar 

  38. Vasilescu, M.A.O., Terzopoulos, D.: Multilinear analysis of image ensembles: Tensorfaces. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 447–460. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  39. Vlasic, D., Brand, M., Pfister, H., Popovi, J.: Face transfer with multilinear models. In: Proc. ACM SIGGRAPH, pp. 426–433 (2005)

    Google Scholar 

  40. Vasilescu, M.A.O., Terzopoulos, D.: Tensortextures: Multilinear image-based rendering. ACM Transactions on Graphics 23, 334–340 (2004)

    Article  Google Scholar 

  41. Cherkassky, V., Mulier, F.: Learning from data: Concepts, Theory and Methods, 2nd edn. Wiley, Chichester (2007)

    Book  MATH  Google Scholar 

  42. Weston, J., Collobert, R., Sinz, F., Bottou, L., Vapnik, V.: Inference with the universum. In: Airoldi, E.M., Blei, D.M., Fienberg, S.E., Goldenberg, A., Xing, E.P., Zheng, A.X. (eds.) ICML 2006. LNCS, vol. 4503, pp. 1009–1016. Springer, Heidelberg (2007)

    Google Scholar 

  43. Sinz, F., Collobert, R., Weston, J., Bottou, L.: Universvm: Support vector machine with large scale cccp functionality, http://www.kyb.tuebingen.mpg.de/bs/people/fabee/universvm.html

  44. Banerjee, A., Dhillon, I.S., Ghosh, J., Sra, S.: Clustering on the unit hypersphere using von mises-fisher distributions. The Journal of Machine Learning Research 6, 1345–1382 (2005)

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Arts, Media and Engineering Program and Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287-8709, USA

    Bo Peng & Gang Qian

  2. Honeywell Labs, 1985 Douglas Drive North, Golden Valley, MN 55422, USA

    Yunqian Ma

Authors
  1. Bo Peng
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  2. Gang Qian
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  3. Yunqian Ma
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

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© 2008 Springer-Verlag Berlin Heidelberg

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Peng, B., Qian, G., Ma, Y. (2008). View-Invariant Pose Recognition Using Multilinear Analysis and the Universum. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_57

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  • DOI: https://doi.org/10.1007/978-3-540-89646-3_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89645-6

  • Online ISBN: 978-3-540-89646-3

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