Human Upper Body Pose Recognition Using Adaboost Template for Natural Human Robot Interaction

In this paper, we propose a novel Adaboost template to recognize human upper body poses from disparity images for natural human robot interaction (HRI). First, the upper body poses of standing persons are classified into seven categories of views. For each category, a mean template, variance template, and percentage template are generated. Then, the template region is divided into positive and negative regions, corresponding to the region of bodies and surrounding open space. A weak classifier is designed for each pixel in the template. A new EM-like Adaboost learning algorithm is designed to learn the Adaboost template. Different from existing Adaboost classifiers, we show that the Adaboost template can be used not only for recognition but also for adaptive top-down segmentation. By using Adaboost template, only a few positive samples for each category are required for learning. Comparison with conventional template matching techniques has been made. Experimental results show that significant improvements can be achieved in both cases. The method has been deployed in a social robot to estimate human attentions to the robot in real-time human robot interaction.