Person Tracking From a Dynamic Balancing Platform

Abstract

Recently, we have begun investigating a new robot soccer domain built around the concept of human-robot teams in a `peer' setting. One of the key challenges for addressing effective human-robot interaction is to robustly identify and track people and robot teammates without requiring undue prior knowledge of their appearance. For cost and complexity reasons, our robots are equipped with monocular color cameras. Thus, we seek an algorithm to enable reliable acquisition and tracking of people and robots from a robot armed with a monocular color camera. We have developed a novel algorithm for acquiring and tracking a single human subject from a dynamically balancing platform, a Segway RMP robot, using a monocular color camera. Our technique uses a combination of known vision and tracking techniques including region growing, motion detection, and mean-shift color-template tracking. In this paper, we describe our approach, and analyze its performance and limitations, for both acquiring and tracking a single human target in an indoor environment. Our experiments demonstrate that acquisition and tracking are feasible with a monocular camera even for a dynamically balancing platform. Moreover, our results show that with current processor technology real-time tracking and robot response are achievable.