Pedestrian detection in images via cascaded L1-norm minimization learning method

Abstract

A new cascaded L1-norm minimization learning (CLML) method for pedestrian detection in images is proposed in this paper. The proposed CLML method, which is designed from the perspective of Vapnic's theory in the statistical learning, integrates feature selection with classifier construction via solving meaningful optimization models. The method incorporates three stages: weak classifier learning, strong classifier learning and the cascaded classifier construction. In the weak classifier learning, the L1-norm minimization learning (LML) and min-max penalty function model are presented. In the strong classifier learning, an integer programming optimization model is built, equaling the reformulation of LML in the integer space. Finally, a cascade of LML classifiers is constructed to promote detection speed. During the classifier learning and pedestrian detection, Histograms of Oriented Gradients of variable-sized blocks (v-HOG) are used as feature descriptors. Experimental results on the INRIA and SDL human datasets show that the proposed method achieves a higher performance and speed than the state-of-the-art methods.