Estimation of human upper body orientation for mobile robotics using an SVM decision tree on monocular images

In this paper, we present a monocular, texture-based method for person detection and upper-body orientation classification. We build on a commonly used approach for person recognition that uses a Support Vector Machine (SVM) on Histograms of Oriented Gradients (HOG) [1] but replace the SVM by a decision tree with SVMs as binary decision makers. Thereby, in addition to the pure detection of persons, the distinction of eight upper-body orientation classes is enabled. The detection of humans and the estimation of their upper-body orientation from larger distances is essential for socially acceptable navigation of mobile robots. It permits to estimate the human´s notice of the robot or even the human´s interest in an interaction. Thus, it is the basis for the decision whether to approach or to avoid a human. By using an SVM decision tree for upper-body orientation estimation in discrete steps of 45°, we were able to classify about 64% of the test samples with an absolute error of less than 22.5°. This performance is much better than the results we obtained with comparable methods. Furthermore, our approach proved to be faster than the other state-of-the-art methods. This is of high relevance for implementation on mobile robots with limited computational resources.