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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29414

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View-Point Insensitive Human Pose Recognition using Neural Network and CUDA
Although lots of research work has been done for human pose recognition, the view-point of cameras is still critical problem of overall recognition system. In this paper, view-point insensitive human pose recognition is proposed. The aims of the proposed system are view-point insensitivity and real-time processing. Recognition system consists of feature extraction module, neural network and real-time feed forward calculation. First, histogram-based method is used to extract feature from silhouette image and it is suitable for represent the shape of human pose. To reduce the dimension of feature vector, Principle Component Analysis(PCA) is used. Second, real-time processing is implemented by using Compute Unified Device Architecture(CUDA) and this architecture improves the speed of feed-forward calculation of neural network. We demonstrate the effectiveness of our approach with experiments on real environment.
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[1] Y. Sagawa, M. Shimosaka, T. Mori and T. Sato, "Fast Online Human Pose Estimation via 3D Voxel Data", Intelligent Robots and Systems, pp. 1034-1040, 2007.
[2] Catherine A., Xingtai Q., Arash M., Maurice., "A novel approach for recognition of human actions with semi-global features", Machine Vision and Applications, 2008, pp. 27-34, 2008.
[3] M. Voit, K. Nickel, R. Stiefelhagen, "Neural Network-Based Head Pose Estimation and Multi-view Fusion", LNCS 4122, pp. 291-298, 2007.
[4] C. Yuan, H. Niemann, "Neural networks for the recognition and pose estimation of 3D objects from a single 2D perspective view", Image and Vision Computing 19, pp. 585-592, 2001.
[5] A. Agarwal, B. Triggs, "Human Pose from Silhouettes by Relevance Vector Regression", CVPR, vol2., pp882-888, 2004.
[6] R. Rosales, S. Sclaroff, "Learning and synthesizing human body motion and posture", IEEE, 2000.
[7] F. Lb, R. Nevatia, "Single View Human Action Recognition using Key Pose Matching and Viterbi Path Searching", CVPR, 2007.
[8] H. Yu, G. Sun, W. Song, X. Li, "Human Motion Recognition Based on Neural Network", IEEE, Vol. 2, pp. 982, 2005.
[9] M. Voit, K. Nicket, R. Stiefelhagen, "Multi-view Head Pose Estimation using Neural Networks", Computer and Robot Vision, pp. 347-352, 2005.
[10] B. Oh, "Face recognition by using neural network classifiers based on PCA and LDA" IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1699-1703, Oct. 2005.
[11] A. Park, K. Jung, "Neural Networks Implementation using CUDA and OpenMP", DICTA 2008 (Digital Image Computing: Techniques and Applications) December 1st, 2008, pp. 155-161.
[12] Van den Bergh, M. Koller-Meier, E. Van Gool, "Fast body posture estimation using volumetric features". In IEEE visual motion computing, 2008
[13] B. Peng, G. Qian, Y. Ma, "View-Invariant Pose Recognition Using Multilinear Analysis and the Universum", ISVC 2008, Part II, LNCS 5359, pp.581-591, 2008.
[14] M. Kortgen, G.-J. Park, M. Novotni, and R. Klein, "3D Shape Matching with 3D Shape Contexts," in Proc. WSCG, 2003
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