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.
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Yang, J., Wang, J., Lu, H. (2010). People Detection by Boosting Features in Nonlinear Subspace. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15702-8_21
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DOI: https://doi.org/10.1007/978-3-642-15702-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15701-1
Online ISBN: 978-3-642-15702-8
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