Abstract
In this chapter we apply the Local Binary Pattern on Three Orthogonal Planes (LBP-TOP) descriptor to the field of human action recognition. We modified this spatio-temporal descriptor using LBP and CS-LBP techniques combined with gradient and Gabor images. Moreover, we enhanced its performaces by performing the analysis on more slices located at different time intevals or at different views. A video sequence is described as a collection of spatial-temporal words after the detection of space-time interest points and the description of the area around them. Our contribution has been in the description part, showing LBP-TOP to be 1) a promising descriptor for human action classification purposes and 2) we have developed several modifications and extensions to the descriptor in order to enhance its performance in human motion recognition, showing the method to be computationally efficient.
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References
Ahonen, T., Hadid, A., Pietikainen, M.: Face Recognition with Local Binary Patterns (2004)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)
Ali, S., Basharat, A., Shah, M.: Chaotic invariants for human action recognition. In: IEEE International Conference on Computer Vision, vol. 0, pp. 1–8 (2007)
Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(3), 257–267 (2001)
Cai, D., He, X.F., Han, J., Zhang, H.J.: Orthogonal laplacianfaces for face recognition, vol. 15(11), pp. 3608–3614 (2006)
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior Recognition via Sparse Spatio-Temporal Features, pp. 65–72 (2005)
Efros, A.A., Berg, A.C., Mori, G., Malik, J.: Recognizing Action at a Distance, vol. 2, pp. 726–733 (2003)
Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recogn. 42(3), 425–436 (2009)
Kellokumpu, V., Zhao, G., Pietikäinen, M.: Human activity recognition using a dynamic texture based method. In: British Machine Vision Conference (2008)
Laptev, I., Lindeberg, T.: Space-time interest points. In: ICCV, pp. 432–439 (2003)
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning Realistic Human Actions from Movies, pp. 1–8 (June 2008)
Mattivi, R., Shao, L.: Human action recognition using lbp-top as sparse spatio-temporal feature descriptor. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 740–747. Springer, Heidelberg (2009)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions, vol. 29(1), pp. 51–59 (January 1996)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Ramanan, D., Forsyth, D.A.: Automatic annotation of everyday movements. In: NIPS. MIT Press, Cambridge (2003)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local svm approach. In: ICPR 2004: Proceedings of the Pattern Recognition, 17th International Conference on (ICPR 2004), Washington, DC, USA, vol. 3, pp. 32–36. IEEE Computer Society, Los Alamitos (2004)
Shan, C., Gong, S., McOwan, P.W.: A comprehensive empirical study on linear subspace methods for facial expression analysis. In: CVPRW 2006: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop, Washington, DC, USA, p. 153. IEEE Computer Society, Los Alamitos (2006)
Shechtman, E., Irani, M.: Space-time behavior based correlation, vol. 1, pp. 405–412 (2005)
Takala, V., Ahonen, T., Pietikainen, M.: Block-based methods for image retrieval using local binary patterns, pp. 882–891 (2005)
Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes (November/December 2006)
Yilmaz, A., Shah, M.: Recognizing human actions in videos acquired by uncalibrated moving cameras. In: IEEE International Conference on Computer Vision, vol. 1, pp. 150–157 (2005)
Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 915–928 (2007)
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Mattivi, R., Shao, L. (2011). Spatio-temporal Dynamic Texture Descriptors for Human Motion Recognition. In: Zhang, J., Shao, L., Zhang, L., Jones, G.A. (eds) Intelligent Video Event Analysis and Understanding. Studies in Computational Intelligence, vol 332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17554-1_4
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DOI: https://doi.org/10.1007/978-3-642-17554-1_4
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