Real-time upper-body detection and orientation estimation via depth cues for assistive technology

Abstract

Automatic and efficient human pose estimation has great practical value in video surveillance. In this paper, we explore how a consumer depth sensor can assist with upper-body detection and pose estimation more precisely in the field of assistive technology for people with disabilities, and a novel real-time upper-body pose (orientation) estimation method is presented. At first, the Haar cascade based upper-body detection is conducted, and the depth information in a fixed subregion is extracted as the input feature vector. Then, support vector machine (SVM) and naive Bayes classifier are compared for estimating the upper-body orientation. Further, in order to acquire the continuous estimation data during a long time for behavioral analysis, we also adopt the support vector regression (SVR) to train a regression model. The experimental results show the effectiveness of the proposed method.