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.
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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)
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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
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DOI: https://doi.org/10.1007/978-3-540-89682-1_11
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