Real-time hand detection based on multi-stage HOG-SVM classifier

In this paper, we propose a real-time hand detection method with multi-stage HOG-SVM classifier. Unlike traditional methods based on learning which make decomposition of feature vector or combination of different types of features or classifiers, upon the division of background into several categories, we propose a multi-stage classifier which combines several SVM classifies each of which is trained to distinguish corresponding divisions of background and target. Furthermore, in order to improve speed performance, skin color information and integral histogram are also applied. Experiment results demonstrate that the proposed algorithm works well under multiple challenging backgrounds in real-time speed (16 frames per second).