Abstract
In this paper, we propose a hierarchical action recognition system applying Fisher discrimination dictionary learning via sparse representation classifier. Feature vectors used to represent certain actions are first generated by employing local features extracted from motion field maps. Sparse representation classification (SRC) are then employed on those feature vectors, in which a structured dictionary for classification is learned applying Fisher discrimination dictionary learning (FDDL). We tested our algorithms on Weizmann human database and KTH human database, and compared the recognition rates with other modeling methods such as k-nearest neighbor. Results showed that the action recognition system applying FDDL can achieve better performance despite that the learning stage for the Fisher discrimination dictionary can converge within only several iterations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bao, R., Shibata, T.: A gesture perception algorithm using compact one-dimensional representation of spatio-temporal motion-field patches. In: 3rd International Conference on Signal Processing and Communication Systems, ICSPCS 2009, pp. 1–5. IEEE (2009)
Bao, R., Shibata, T.: Spatio-Temporal Motion Field Descriptors for The Hierarchical Action Recognition System. In: 5th International Conference on Signal Processing and Communication Systems, ICSPCS 2011. IEEE (2011)
Candès, E.: Compressive sampling. In: Proceedings oh the International Congress of Mathematicians, Madrid, August 22-30, pp. 1433–1452 (2006) (invited lectures)
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: VS-PETS, pp. 65–72 (2005)
Efros, A.A., Berg, A.C., Berg, E.C., Mori, G., Malik, J.: Recognizing action at a distance. In: ICCV, pp. 726–733 (2003)
Feng, X., Perona, P.: Human action recognition by sequence of movelet codewords. In: Proceedings of the First International Symposium on 3D Data Processing Visualization and Transmission, pp. 717–721. IEEE (2002)
Ferrari, V., Marin-Jimenez, M., Zisserman, A.: Progressive search space reduction for human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: ICCV, pp. 1395–1402 (2005)
Hampapur, A., Brown, L., Connell, J., Ekin, A., Haas, N., Lu, M., Merkl, H., Pankanti, S.: Smart video surveillance: exploring the concept of multiscale spatiotemporal tracking. IEEE Signal Processing Magazine 22(2), 38–51 (2005)
Jhuang, H., Serre, T., Wolf, L., Poggio, T.: A biologically inspired system for action recognition. In: ICCV, pp. 1–8 (2007)
Liu, J., Shah, M.: Learning human actions via information maximization. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
Mairal, J., Elad, M., Sapiro, G.: Sparse representation for color image restoration. IEEE Transactions on Image Processing 17(1), 53–69 (2008)
Meng, H., Pears, N., Bailey, C.: A human action recognition system for embedded computer vision application. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–6. IEEE (2007)
Niebles, J., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision 79(3), 299–318 (2008)
Nowozin, S., Bakir, G., Tsuda, K.: Discriminative subsequence mining for action classification. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE (2007)
Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976–990 (2010)
Quiroga, R., Reddy, L., Kreiman, G., Koch, C., Fried, I.: Invariant visual representation by single neurons in the human brain. Nature 435(7045), 1102–1107 (2005)
Ramanan, D.: Learning to parse images of articulated bodies. In: NIPS 2007. NIPS (2006)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local svm approach. In: ICPR (2004)
Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D.: Conditional models for contextual human motion recognition. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1808–1815. IEEE (2005)
Touyama, H., Aotsuka, M., Hirose, M.: A pilot study on virtual camera control via Steady-State VEP in immersing virtual environments. In: Proceedings of the Third IASTED International Conference on Human Computer Interaction, pp. 43–48. ACTA Press (2008)
Vogler, C., Metaxas, D.: Handshapes and Movements: Multiple-Channel American Sign Language Recognition. In: Camurri, A., Volpe, G. (eds.) GW 2003. LNCS (LNAI), vol. 2915, pp. 247–258. Springer, Heidelberg (2004)
Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 210–227 (2008)
Yang, M., Zhang, L., Feng, X., Zhang, D.: Fisher discrimination dictionary learning for sparse representation. In: ICCV. IEEE (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bao, R., Shibata, T. (2012). A Hierarchical Action Recognition System Applying Fisher Discrimination Dictionary Learning via Sparse Representation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-29347-4_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29346-7
Online ISBN: 978-3-642-29347-4
eBook Packages: Computer ScienceComputer Science (R0)