A Novel Human Pose Detection from Videos Algorithm Based on Motion Capture Data

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This video image of static background frame and deduction, the pixel, pixels for sports change monitoring and static pixels. By combining the feature of deformation of human body positioning movement of template, the human body pose detection algorithm put in spatio-temporal detection to human pose recognition using feature matching, accelerate matching speed probability. This method in the testing result is superior to other pose recognition algorithm, and also has the ability to quickly identify.

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Periodical:

Edited by:

Qi Luo

Pages:

833-837

DOI:

10.4028/www.scientific.net/AMM.20-23.833

Citation:

O. Y. Yi "A Novel Human Pose Detection from Videos Algorithm Based on Motion Capture Data ", Applied Mechanics and Materials, Vols. 20-23, pp. 833-837, 2010

Online since:

January 2010

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$38.00

[1] D. Greig, B. Porteous, and A. Seheult. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society, Series B, 51(2): 271-279, (1989).

[2] A.A. Efros, A.C. Berg, G. Mori, and J. Malik. Recognizing action at a distance. In International Conference on Computer Vision, pages 726-733, October (2003).

DOI: 10.1109/iccv.2003.1238420

[3] A. Elgammal and C.S. Lee. Inferring 3D Body Pose from Silhouettes using Activity Manifold Learning. In CVPR, 2004, June (2004).

DOI: 10.1109/cvpr.2004.1315230

[4] P. Felzenszwalb and D. Huttenlocher. Pictorial Structures for Object Recognition. International Journal of Computer Vision, 16(1), (2005).

[5] D. Gavrila, J. Giebel, and S. Munder. Vision-based pedestrian detection: the protector system. In Intelligent Vehicles Symposium, pages 13-18, (2004).

DOI: 10.1109/ivs.2004.1336348

[6] J. Giebel, D.M. Gavrila, and C. Schnorr. A bayesian framework for multi-cue 3d object tracking. In Proceedings of European Conference on Computer Vision, (2004).

DOI: 10.1007/978-3-540-24673-2_20

[7] R. Gross and J. Shi(2001) The CMU motion of body (MoBo) database. Technical Report CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon University.

[8] D.P. Huttenlocher, G.A. Klanderman, W.J. Rucklidge. Comparing images using the Hausdorff distance, IEEE Trans. Pattern Anal. Machine Intell., vol. 15, pp.850-863, Sept. (1993).

DOI: 10.1109/34.232073

[9] V. DiGesu andV.V. Starovoitov, Distance-based functions for image comparison, Pattern Recogn. Lett., vol. 20, pp.207-213, Feb. (1999).

[10] M.P. Dubuisson and A.K. Jain, A modified Hausdorff distance for object matching, in Proc. 12 th Int. Conf. Pattern Recognition, 1994, pp.566-568.

[11] J. Paumard, Robust comparison of binary images, Pattern Recogn. Lett., vol. 8, pp.425-429, Mar. (1999).

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