In this project, we present an approach that allows the Georgia Tech Miniature Autonomous Blimp (GT-MAB) to detect and follow a human. This accomplishment is the first Human Robot Interaction (HRI) demonstration between an uninstrumented human and a robotic blimp. GT-MAB is an ideal platform for HRI missions because it is safe to humans and can support sufficient flight time for HRI experiments. However, due to complex aerodynamic influence on the blimp, the human following task for GT-MAB with a single on-board camera is a challenging problem. We integrate face detection and KLT feature tracker algorithms to achieve robust face detecting and tracking. Based on the face center detected in the real-time video stream, we estimated the 3D positions of the human with respect to GT-MAB. Vision-based PID controllers are designed based on estimated relative position and the motion primitives of GT-MAB such that it can achieve stable and continuous human following behavior. Experimental results are presented to show the human following capability on GT-MAB. Video available.