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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.