A new person tracking method for human-robot interaction intended for mobile devices

Abstract

People detection and tracking are essential capabilities in human-robot interaction. However, the development of these tasks is specially difficult in cluttered environments where it is not possible to create a background model because of the robot movement. To detect and track people in a scene the use of vision sensors is convenient in order to distinguish people from other objects with similar shapes. This paper presents a novel approach for person tracking which combines depth, color and gradient information based on stereo vision. The degree of confidence assigned to depth information in the tracking process varies according to the amount of it found in the disparity map. A novel confidence measure is defined for it. To test the validity of our proposal, it is evaluated in several color-with-depth sequences where people interact in complex situations. © Springer-Verlag Berlin Heidelberg 2007.