[1]LIN Haibo,WANG Hao,ZHANG Yi.Human postures recognition based on the improved Gauss kernel function[J].CAAI Transactions on Intelligent Systems,2015,10(3):436-441.[doi:10.3969/j.issn.1673-4785.201405049]
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Human postures recognition based on the improved Gauss kernel function

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 3 Page number: 436-441 Column: 学术论文—机器感知与模式识别 Public date: 2015-06-25
Title:
Human postures recognition based on the improved Gauss kernel function
Author(s):
LIN Haibo;  WANG Hao;  ZHANG Yi
Research Center of Intelligent System and Robot, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Keywords:
human postures; recognition; Gauss kernel function; Kinect; Euclidean distance; geodesic distance; support vector machines (SVM)
CLC:
TP391.9
DOI:
10.3969/j.issn.1673-4785.201405049
Abstract:
In this paper, a method based on the joint angles of human postures is proposed in order to improve the human posture recognition rate through building a human skeleton model on the Kinect platform. For the traditional method of human postures recognition, Euclidean distance is used in Gaussian kernel function, but the positional relationship of sample point and test point of human body joint can not be reflected completely. So the method of improved Gaussian kernel function and multi-class support vector machines (MSVM) is proposed. Using the geodesic distance instead of the Euclidean distance in the Gaussian radial basis kernel function, a posture kernel function based on the geodesic distance is established. Using the binary tree method, a multi-class support vector machine is built to complete classification of 12 kinds of upper limb postures. Experimental results showed that the improved algorithm can identify body postures more effectively than before, achieving a good recognition effect.
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