Person tracking on a mobile robot with heterogeneous inter-characteristic feedback

Abstract

For a mobile robot that interacts with humans such as a home assistant or a tour guide robot, tracking a particular person among multiple persons is a fundamental, yet challenging task. Uniquely identifying characteristics such as a person's face, may not be visible consistently enough to be used as the sole form of identification. Rather, it may be useful to also track more frequently visible, but perhaps less uniquely identifying characteristics such as a person's clothes. After learning various characteristics of a person, the tracking system is required to autonomously update itself with additional training data, since the learned features may change over space and time due to the mobile nature of the robot. In this paper, we introduce a novel algorithm for merging multiple, heterogeneous sub-classifiers designed to track and associate different characteristics of a person being tracked. These heterogeneous classifiers give feedback to each other by identifying additional online training data for one another, thus improving the performance of each classifier and the accuracy of the overall system. Our algorithm has been fully implemented and tested on a Segway base.