Human Pose Estimation Based on Fusion of HOG and Color Feature
HAN Gui-Jin1,2, ZHU Hong1
1.School of Automation and Information Engineering, Xi′an University of Technology, Xi′an 710048 2.School of Automation, Xi′an University of Posts and Telecommunications, Xi′an 710121
Abstract：Appearance model plays an important role in the human pose estimation. To improve the estimation accuracy, how to set up the appearance model by using the histogram of oriented gradient (HOG) and color features is studied. The sub-classifier for each cell unit of the body part is built by using the support vector data description (SVDD) algorithm, and then the HOG-based appearance model is constructed by the linear combination of sub-classifiers with different weights. The specific location probability is learned by using those states, which has higher similarity with the HOG-based appearance model, and the corresponding color histogram is calculated, which is the color-based appearance model. According to the illumination and the color contrast between the clothing and background in the static image to be proceeded, the weights of two appearance models are determined, and then two appearance models are combined linearly to construct appearance model based on the fusion of the HOG and color features. The proposed appearance model is used for human pose estimation, and the experimental results show it is more effectively and gets higher pose estimation accuracy.
韩贵金，朱虹. 基于HOG和颜色特征融合的人体姿态估计*[J]. 模式识别与人工智能, 2014, 27(9): 769-777.
HAN Gui-Jin, ZHU Hong. Human Pose Estimation Based on Fusion of HOG and Color Feature. , 2014, 27(9): 769-777.
[1] Felzenszwalb P F, Huttenlocher D P. Pictorial Structures for Object Recognition. International Journal of Computer Vision, 2005, 61(1): 55-79 [2] Moeslund T B, Hilton A, Krüger V, et al. Visual Analysis of Humans. London, UK, Springer-Verlag, 2011 [3] Fischler M A, Elschlager R A. The Representation and Matching of Pictorial Structures. IEEE Transactions on Computers, 1973, c-22(1): 67-92 [4] Sapp B, Toshev A, Taskar B. Cascaded Models for Articulated Pose Estimation // Proc of the 11th European Conference on Computer Vision. Heraklion, Greece, 2010: 406-420 [5] Johnson S, Everingham M. Combining Discriminative Appearance and Segmentation Cues for Articulated Human Pose Estimation // Proc of the 12th IEEE International Conference on Computer Vision Workshops. Kyoto, Japan, 2009: 405-412 [6] Han G J, Zhao Y. Human Pose Estimation Based on Tree-Like Picture Model. Journal of Xi′an University of Posts and Telecommunications, 2013, 18(3): 83-86 (in Chinese) (韩贵金,赵 勇.基于树形图结构模型的人体姿态估计.西安邮电大学学报, 2013, 18(3): 83-86) [7] Ukita N. Articulated Pose Estimation with Parts Connectivity Using Discriminative Local Oriented Contours // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012: 3154-3161 [8] Eichner M, Ferrari V. Better Appearance Models for Pictorial Structures // Proc of the 20th British Machine Vision Conference. London, UK, 2009: 3.1-3.11 [9] Ferrari V, Marin-Jimenez M, Zisserman A. Progressive Search Space Reduction for Human Pose Estimation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA, 2008: 1-8 [10] Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005: 886-893 [11] Srinivasan P, Shi J B. Bottom-Up Recognition and Parsing of the Human Body // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA, 2007: 824-831 [12] Johnson S A. Articulated Human Pose Estimation in Natural Images. Ph.D Dissertation. Leeds, Britain: University of Leeds, 2012 [13] David M J T, Robert P W D. Support Vector Data Description. Machine Learning, 2004, 54(1): 45-66 [14] Xiao Y S, Liu B, Cao L B, et al. Multi-sphere Support Vector Data Description for Outliers Detection on Multi-distribution Data // Proc of the IEEE International Conference on Data Mining Workshops. Miami, USA, 2009: 82-87 [15] Xue Z X, Liu S Y, Liu W L, et al. SVDD Based Learning Algorithm with Progressive Transductive Support Vector Machines. Pattern Recognition and Artificial Intelligence, 2008, 21(6): 721-727 (in Chinese) (薛贞霞,刘三阳,刘万里.基于SVDD的渐进直推式支持向量机学习算法.模式识别与人工智能, 2008, 21(6): 721-727) [16] Han G J, Zhu H. Human Pose Estimation Algorithm Based on Pictorial Structure Model. Computer Engineering and Applications, 2013, 49(14): 30-33 (in Chinese) (韩贵金,朱 虹.一种基于图结构模型的人体姿态估计算法.计算机工程与应用, 2013, 49(14): 30-33) [17] Wu M R, Ye J P. A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers. IEEE Tran on Pattern Analysis and Machine Intelligence, 2009, 31(11): 2088-2092 [18] Jiang H. Human Pose Estimation Using Consistent Max-Covering // Proc of the IEEE 12th International Conference on Computer Vision. Kyoto, Japan, 2009: 1357-1364 [19] Tian T P, Sclaroff S. Fast Globally Optimal 2D Human Detection with Loopy Graph Models // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 81-88 [20] Singh V K, Nevatia R, Huang C. Efficient Inference with Multiple Heterogeneous Part Detectors for Human Pose Estimation // Proc of the 11th European Conference on Computer Vision. Heraklion, Greece, 2010: 314-327
2014, Vol. 27 
