Human detection in complex real scenes based on combination of biorthogonal wavelet transform and Zernike moments

Abstract

Human detection in real scenes with high complexity is a crucial problem in�computer vision�research. For tackling the human detection task, several existing methods adopt one feature or combination of features to detect human objects. In this work, we propose a new combination of features based algorithm for human detection, which identifies the presence of a human, in complex real scenes. In the combination, the two features (i) biorthogonal�wavelet transform�(BWT) and (ii) Zernike moments (ZM) have been used. The approximate shift-invariance and�symmetry properties�of BWT facilitate the human detection in the�wavelet domain. Specifically, the shift-invariance property of BWT is effective for translated object representation whereas the symmetry property yields perfect reconstruction for retaining object boundaries (i.e., edges). Moreover, translation and rotation-invariance properties of ZM are especially beneficial for the representation of varying pose and orientation of the human objects. For these reasons, the composite of the two features brings about significant synthesized benefits over each�single feature�and the other widely used features. In the experiments for human detection, we used two classifiers,�AdaBoost�and�support vector machines, respectively, for the comparative study purpose, and the standard INRIA dataset and DaimlerChrysler dataset were used for the evaluations. Experimental results demonstrated the significant outperformance of the proposed method through�quantitative evaluations�and also suggest that the proposed hybridization of features is preferable for the classification problem.