Related HOG Features for Human Detection Using Cascaded Adaboost and SVM Classifiers

Robust and fast human detection in static image is very important for real applications. Although different feature descriptors have been proposed for human detection, for HOG descriptor, how to select and combine more distinguish block-based HOGs, and how to simultaneously make use of the correlation and the local information of these selected HOGs still lack enough research and analysis. In this paper, we present a set of Related HOG (RHOG) features, including distinctive block-based HOGs (Ele-HOGs) which are selected by Adaboost and a global HOG descriptor which is concatenated by Ele-HOGs (CSele-HOG). Ele-HOG can discriminatively describe local distribution of human object while CSele-HOG contains global information. In addition, we propose a novel human detection framework of Cascaded Adaboost and SVM classifiers (CAS) based on RHOG features, which combines the advantages of Adaboost and SVM classifiers. Experimental results on INRIA dataset demonstrate the effectiveness of the proposed method.