Real-time person orientation estimation and tracking using colored point clouds

Abstract

Robustly estimating the orientations of people is a crucial precondition for a wide range of applications. Especially for autonomous systems operating in populated environments, the orientation of a person can give valuable information to increase their acceptance. Given people’s orientations, mobile systems can apply navigation strategies which take people’s proxemics into account or approach them in a human like manner to perform human robot interaction (HRI) tasks. In this paper, we present an approach for person orientation estimation based on computationally efficient features extracted from colored point clouds, formerly used for a two-class person attribute classification. The classification approach has been extended to the continuous domain while treating the problem of orientation estimation in real time. Furthermore, we present an approach for tracking estimated orientations over time using a Bayesian filter. We will show that tracking can increase the accuracy of orientations by up to 3.69° on a dataset recorded with a mobile robot. Best results on this highly challenging dataset are achieved with a regression approach for orientation estimation in combination with tracking. The mean angular error of just 16.49° proofs the applicability in real-world scenarios.