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Extraction of Discriminative Patterns from Skeleton Sequences for Accurate Action Recognition

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Emergence of novel techniques devices e.g., MS Kinect, enables reliable extraction of human skeletons from action videos. Taking skeleton data as inputs, we propose an approach to extract the discriminative patterns for efficient human action recognition. Each action is considered to consist of a sequence of unit actions, each of which is represented by a pattern. Given a skeleton sequence, we first automatically extract the key-frames, and then categorize them into different patterns. We further use a statistical metric to evaluate the discriminative capability of patterns, and define them as local features for action recognition. Experimental results show that the extracted local descriptors could provide very high accuracy in the action recognition, which demonstrate the efficiency of our method in extracting discriminative unit actions.
Wydawca
Rocznik
Strony
247--261
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
autor
  • School of Information Science, Japan Advanced Institute of Science and Technology, Japan
autor
  • School of Information Science, Japan Advanced Institute of Science and Technology, Japan
autor
  • School of Information Science, Japan Advanced Institute of Science and Technology, Japan
autor
  • Faculty of Information Technology, University of Science, VNU - Ho Chi Minh City, Vietnam
Bibliografia
  • [1] Pascal S., Mineau G. W., Beyond TFIDF Weighting for Text Categorization in the Vector Space Model, International Joint Conference on Artificial Intelligence. Edinburgh, Scotland: UK, pp. 1130-1135, 2005.
  • [2] Krystian M., Hirofumi U., Action recognition with appearance-motion features and fast search trees, Computer Vision and Image Understanding, Volume 115 Issue 3, pp.426-438, March 2011.
  • [3] Kovar L., Gleicher M., Automated extraction and parameterization of motions in large data sets, ACM Transactions on Graphics 23, 3 (2004), 559568. SIGGRAPH, 2004.
  • [4] Krüger B., Tautges J., Weber A., and Zinke A., Fast local and global similarity searches in large motion capture databases, 2010 ACM SIGGRAPH, pp.1-10. Eurographics Association, 2010.
  • [5] Forbes K. and Fiume E., An efficient search algorithm for motion data using weighted PCA, In ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp.67-76, ACM Press, 2005.
  • [6] Alla S., Jessica K. H., Construction and optimal search of interpolated motion graphs, ACM Transactions on Graphics Journal, SIGGRAPH 2007 Proceedings, August 2007 .
  • [7] Muller M, Information Retrieval for Music and Motion, ISBN: 978-3-540-74047-6, Springer, 2007.
  • [8] Arikan O. and Forsyth. D. A., Interactive motion generation from examples, In SIGGRAPH, pp.483-490, New York, NY, USA, ACM Press, 2002.
  • [9] Egges A., Molet T., and Magnenat-Thalmann N., Personalised real-time idle motion synthesis, In Pacific Graphics, IEEE Computer Society, pp.121-130, Washington, DC, USA, 2004.
  • [10] Kovar L., Gleicher M., and Pighin F., Motion graphs, In SIGGRAPH, pp.473-482, New York, NY, USA, ACM Press, 2002.
  • [11] Kilner J., Guillemaut J.Y., Hilton A., 3D action matching with key-pose detection. Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference, Kyoto, pp.1-8, 2009.
  • [12] Baak A., Mller M., Seidel H.P., An Efficient Algorithm for Keyframe-based Motion Retrieval in the Presence of Temporal Deformations, The 1st ACM SIGMM Int. Conf. on Multimedia Information Retrieval, 2008.
  • [13] Sun X., Chen M.-Y., and Hauptmann A., Action Recognition via Local Descriptors and Holistic Features, IEEE - CVPR for Human Communicative Behaviour Analysis, Miami Beach, Florida, USA, June 25, 2009.
  • [14] Mikolajczyk K., and Uemura H., Action recognition with appearance-motion features and fast search trees, Computer Vision and Image Understanding, Volume 115 Issue 3, pp.426-438, March 2011.
  • [15] Ankerst M., Kastenmller G., Kriegel H. P., and Seidl T., 3D shape histograms for similarity search and classification in spatial databases, Advances in Spatial Databases, 6th International Symposium, SSD99, 1651, pp.207-228, 1999.
  • [16] Huang P., Hilton A. and Starck J., Shape Similarity for 3D Video Sequences of People, In International Journal of Computer Vision (IJCV) special issue on 3D Object Retrieval, Volume 89, Issue 2-3, pp.362-381, September 2010.
  • [17] Huang P., Hilton A. and Starck J., Automatic 3D Video Summarization: Key Frame Extraction from Self-Similarity., 4th International Symposium on 3D Data Processing, pp.71-78, Atlanta, GA, USA, June 2008.
  • [18] Wilson E.B., Probable Inference, the Law of Succession, and Statistical Inference, Journal of the American Statistical Association, 22, pp.209-212, 1927.
  • [19] Motion Capture Database HDM05, http://www.mpi-inf.mpg.de/resources/HDM05/, 2012
  • [20] Supplemental Materials, http://www.jaist.ac.jp/_s1020210/FI.htm, 2013.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3cd8fc48-d325-4885-a846-efb348bac3da
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