People detection by boosting features in nonlinear subspace

Authors:
Jie Yang;Jinqiao Wang;Hanqing Lu
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Year:
2010

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Abstract

In this paper, we propose a novel approach to detect people by boosting features in the nonlinear subspace. Firstly, three types of the HOG (Histograms of Oriented Gradients) descriptor are extracted and grouped into one descriptor to represent the samples. Then, the nonlinear subspace with higher dimension is constructed for positive and negative samples respectively by using Kernel PCA. The final features of the samples are derived by projecting the grouped HOG descriptors onto the nonlinear subspace. Finally, AdaBoost is used to select the discriminative features in the nonlinear subspace and train the detector. Experimental results demonstrate the effectiveness of the proposed method.