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.
Human Detection from a Mobile Robot Using Fusion of Laser and Vision Information
