Intelligent optimal control of thermal vision-based Person-Following Robot Platform

Abstract

In this paper the supervisory control of the Person-Following Robot Platform is presented. The main part of the high level control loop of mobile robot platform is a real-time robust algorithm for human detection and tracking. The main goal was to enable mobile robot platform to recognize the person in indoor environment, and to localize it with accuracy high enough to allow adequate human-robot interaction. The developed computationally intelligent control algorithm enables robust and reliable human tracking by mobile robot platform. The core of the recognition methods proposed is genetic optimization of threshold segmentation and classification of detected regions of interests in every frame acquired by thermal vision camera. The support vector machine classifier determines whether the segmented object is human or not based on features extracted from the processed thermal image independently from current light conditions and in situations where no skin color is visible. Variation in temperature across same objects, air flow with different temperature gradients, person overlap while crossing each other and reflections, put challenges in thermal imaging and will have to be handled intelligently in order to obtain the efficient performance from motion tracking system.