PDFAbstract: In this paper, we decompose motion flow using multidirectional Gabor filter bank to create robust descriptors for action recognition problem. The dense trajectory is utilized to allocate the Region of Interest (ROI) for extracting Gabor descriptors. In addition, the feature distribution is adopted instead of the conventional sum of energy. Our experiments are conducted in an open data set (UCF11) and our self-constructed one (CNU). The results indicate that the proposed feature outperforms all other ones in both individually and collectively.
Keywords: Gabor histogram, GMM supervector, dense trajectory features REFERENCES [1] M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, "Actions as Space-Time Shapes," Proceeding of Tenth IEEE International Conference on Computer Vision, Beijing, vol. 2, no., pp. 1395 - 1402, Oct. 2005 2005. [2] I. Laptev and T. Lindeberg, "Local Descriptors for Spatio-temporal Recognition," Proceeding of First International Workshop, SCVMA, vol. 3667, no., pp. 91-103, 2004. [3] N. N. Bui and J. Y. Kim, "Human Action Recognition based on GMM-UBM Supervector using SVM with non-linear GMM KL and GUMI," Proceeding of Int. Conf. Digital Image Processing, vol. 9631, no., pp. 96311G-96311G-7, April 2015. [4] J. Liu, J. Luo, and M. Shah, "Recognizing realistic actions from videos "in the wild"," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1996 - 2003, 2009. [5] H. Wang, A. Kläser, C. Schmid, and C.-L. Liu, "Dense Trajectories and Motion Boundary Descriptors for Action Recognition," International N. N. Bui et al. International Journal of Computers http://www.iaras.org/iaras/journals/ijc
Cite this paper
Ngoc Nam Bui, Tan Dat Trinh, Min Kyung Park, Jin Young Kim. (2016) Histogram of Multi-Directional Gabor Filter Bank for Motion Trajectory Feature Extraction. International Journal of Computers, 1 , 83-88
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