Computationally intelligent system for thermal vision people detection and tracking in robotic applications

This paper describes a system for real-time robust segmentation of human in a thermal image used for supervisory control of mobile robot platform. 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 intelligent segmentation and classification of detected regions of interests in every frame acquired by thermal vision camera. Advanced intelligent segmentation algorithm is based on improved fuzzy closed-loop colour region segmentation. This segmentation algorithm enables autonomous functioning of robot system in cluttered environments. The classifier determines whether the segmented object is human or not based on features extracted from the processed thermal image. With this approach a person can be detected independently from current light conditions and in situations where no skin colour is visible. However, 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. Presented research in this field includes making tracking system more robust and reliable by using the computational intelligence.