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Body Pose Estimation Based on Half - body Mixed Model

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DOI: 10.23977/jaip.2016.11004 | Downloads: 92 | Views: 7260

Author(s)

Xinhua Wu 1, Gang Liu 1, Jiuhua Tao 1

Affiliation(s)

1 Computer Science Technology, Wuhan University of Technology, Wuhan, Hubei 430063, China

Corresponding Author

Jiuhua Tao

ABSTRACT

In order to improve the effect and speed of human pose estimation from the static image, this paper proposes a method based on the prior knowledge of HOG eigenvalue and face detection to establish the human body bust mixed model for human pose estimation. First, assume that the bust human model contains K components, the static image is divided into M * N cells, each cell may be one of the components, according to the fractional calculation formula to calculate the root component scores, and ultimately determine the human body. The bodily mixed model can be used to calculate the position and direction of human limb accurately.

KEYWORDS

Human pose estimation; Object detection; HOG feature extraction

CITE THIS PAPER

Jiuhua, T. , Xinhua, W. and Gang L. (2016) Body Pose Estimation Based on Half - body Mixed Model. Journal of Artificial Intelligence Practice (2016) 1: 14-19.

REFERENCES

[1] V. Ferrari, M. J. Marln-Jimnez, A. Zisserman. Pose Search: Retrieving People Using Their Pose. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2009.
[2] E. Marcin, F. Vittorio. Better Appearance Models for Pictorial Structure. Proceedings of British Machine Vision Conference, 2009.
[3] B. Daubney, D. Gibson, N. Campbell. Monocular 3D Human Pose Estimation Using Sparse Motion Features. Proceedings of International Conference on Computer Vision, 1050-1057,2009.
[4] Yi Wang, Gang Qian. Robust Human Pose Recognition Using Unabled Markers. Proceedings of Workshop on Applications of Computer Vision, 1-7, 2008.
[5] P. Kohli, J. Rihan, M. Bray. Simultaneous Segmentation and Pose Estimation of Humans using Dynamic Graph Cuts. International Journals of Computer Vision, 79(3):285-298, 2008.
[6] R. Okada, S. Soatto. Relevant Feature Selection for Human Pose Estimation and Localization in Cluttered Images. Proceedings of European Conference on Computer Vision, 434-445,2008.
[7] nlpr-web.ia.ac.cn/course/object-recognition.pdf
[8] J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, A. Blake. Real-time Human Pose Recognition in Parts from Single Depth Images. Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, 1297-1304, 2011.
[9] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9): 1627-1645, 2010.
[10] P. Felzenszwalb, D. McAllester, D. Ramanan. A Discriminatively Trained, Multiscale, Deformable Part Model. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008.119
[11] P. Felzenszwalb, D. P. Huttenlocher. Distance Transforms of Sampled Functions. Theory of Computing, Vol. 8, 415-428, 2012.
 

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