Abstract
We study the human action recognition problem based on motion features directly extracted from video. In order to implement a fast human action recognition system, we select simple features that can be obtained from non-intensive computation. We propose to use the motion history image (MHI) as our fundamental representation of the motion. This is then further processed to give a histogram of the MHI and the Haar wavelet transform of the MHI. The combination of these two features is computed cheaply and has a lower dimension than the original MHI. The combined feature vector is tested in a Support Vector Machine (SVM) based human action recognition system and a significant performance improvement has been achieved. The system is efficient to be used in real-time human action classification systems.
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
Aggarwal, J.K., Cai, Q.: Human motion analysis: a review. Comput. Vis. Image Underst. 73, 428–440 (1999)
Farnell, B.: Moving bodies, acting selves. Annual Review of Anthropology 28, 341–373 (1999)
Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23, 257–267 (2001)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proc. Int. Conf. Pattern Recognition (ICPR 2004), Cambridge, U.K (2004)
Ke, Y., Sukthankar, R., Hebert, M.: Efficient visual event detection using volumetric features. In: Proceedings of International Conference on Computer Vision, Beijing, China, October 15-21, pp. 166–173 (2005)
Weinland, D., Ronfard, R., Boyer, E.: Motion history volumes for free viewpoint action recognition. In: IEEE International Workshop on modeling People and Human Interaction (PHI 2005) (2005)
Wong, S.F., Cipolla, R.: Real-time adaptive hand motion recognition using a sparse bayesian classifier. In: ICCV-HCI, pp. 170–179 (2005)
Ogata, T., Tan, J.K., Ishikawa, S.: High-speed human motion recognition based on a motion history image and an eigenspace. IEICE Transactions on Information and Systems E89, 281–289 (2006)
Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006)
Oikonomopoulos, A., Patras, I., Pantic, M.: Kernel-based recognition of human actions using spatiotemporal salient points. In: Proceedings of IEEE Int’l Conf. on Computer Vision and Pattern Recognition 2006, vol. 3 (2006)
Meng, H., Pears, N., Bailey, C.: Recognizing human actions based on motion information and svm. In: 2nd IET International Conference on Intelligent Environments, Athens, Greece, IET, pp. 239–245 (2006)
Meng, H., Pears, N., Bailey, C.: Human action classification using svm_2k classifier on motion features. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) MRCS 2006. LNCS, vol. 4105, pp. 458–465. Springer, Heidelberg (2006)
Meng, H., Shawe-Taylor, J., Szedmak, S., Farquhar, J.D.R.: Support vector machine to synthesise kernels. In: Deterministic and Statistical Methods in Machine Learning, 242–255 (2004)
Farquhar, J.D.R., Hardoon, D.R., Meng, H., Shawe-Taylor, J., Szedmak, S.: Two view learning: Svm-2k, theory and practice. In: NIPS (2005)
Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)
Strang, G., Nguyen, T.: Wavelets and Filter Banks. Wellesley Cambridge Press (1996)
Aizerman, A., Braverman, E.M., Rozoner, L.I.: Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25, 821–837 (1964)
Joachims, T.: Making large-scale svm learning practical. In: Oikonomopoulos, A., Patras, I., Pantic, M. (eds.) Advances in Kernel Methods - Support Vector Learning, USA. MIT-Press, Cambridge (1999)
Meng, H., Pears, N., Bailey, C.: A human action recognition system for embedded computer vision application. In: The 3rd IEEE workshop on Embeded Computer Vision, Minneapolis,USA (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meng, H., Pears, N., Bailey, C. (2008). Motion Feature Combination for Human Action Recognition in Video. In: Braz, J., Ranchordas, A., Araújo, H.J., Pereira, J.M. (eds) Computer Vision and Computer Graphics. Theory and Applications. VISIGRAPP 2007. Communications in Computer and Information Science, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89682-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-89682-1_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89681-4
Online ISBN: 978-3-540-89682-1
eBook Packages: Computer ScienceComputer Science (R0)