Kernel PCA of HOG features for posture detection

Authors

Peng Cheng, University of WollongongFollow
Wanqing Li, University of WollongongFollow
Philip Ogunbona, University of WollongongFollow

RIS ID

32099

Publication Details

Cheng, P., Li, W. & Ogunbona, P. (2009). Kernel PCA of HOG features for posture detection. International Conference: Image and Vision Computing New Zealand (pp. 415-420). Wellington, New Zealand: IEEE.

Abstract

Motivated by the non-linear manifold learning ability of the Kernel Principal Component Analysis (KPCA), we propose in this paper a method for detecting human postures from single images by employing KPCA to learn the manifold span of a set of HOG features that can effectively represent the postures. The main contribution of this paper is to apply the KPCA as a non-linear learning and open-set classification tool, which implicitly learns a smooth manifold from noisy data that scatter over the feature space. For a new instance of HOG feature, its distance to the manifold that is measured by its reconstruction error when mapping into the kernel space serves as a criterion for detection. And by combining with a newly developed KPCA approximation technique, the detector can achieve almost real-time speed with neglectable loss of performance. Experimental results have shown that the proposed method can achieve promising detection rate with relatively small size of positive training dataset.