This paper describes a vision-based tracking system using an autonomous Quadrotor Unmanned Micro-Aerial Vehicle (MAV). The vision-based control system relies on color target detection and tracking algorithm using integral image, Kalman filters for relative pose estimation, and a nonlinear controller for the MAV stabilization and guidance. The vision algorithm relies on information from a single onboard camera. An arbitrary target can be selected in real-time from the ground control station, thereby outperforming template and learning-based approaches. Experimental results obtained from outdoor flight tests, showed that the vision-control system enabled the MAV to track and hover above the target as long as the battery is available. The target does not need to be pre-learned, or a template for detection. The results from image processing are sent to navigate a non-linear controller designed for the MAV by the researchers in our group.