Head-and-Shoulder detection in varying pose

Authors:
Yi Sun;Yan Wang;Yinghao He;Yong Hua
Affiliations:
School of Electronic and Information Engineering, Dalian University of Technology, Dalian, P.R.C.;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, P.R.C.;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, P.R.C.;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, P.R.C.
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

Head-and-shoulder detection has been an important research topic in the fields of image processing and computer vision. In this paper, a head-and-shoulder detection algorithm based on wavelet decomposition technique and support vector machine (SVM) is proposed. Wavelet decomposition is used to extract features from real images, and linear SVM and non-linear SVM are trained for detection. Non-head-and-shoulder images can be removed by the linear SVM firstly, and then non-linear SVM detects head-and-shoulder images in detail. Varying head-and-shoulder pose can be detected from frontal and side views, especially from rear view. The experiment results prove that the method proposed is effective and fast to some extent.