This paper describes our current work in developing a vision-based tracking and trajectory prediction system for an aerial robot based on low-cost digital cameras, image processing techniques, and a filtering and prediction algorithm. The system determines the pose (location and orientation) of a miniature airship, online during indoor flight, and will be used in a development framework for a future autonomous flight control system. Object localization is achieved by tracking an infra-red target array mounted to a model airship. Its pose in three-dimensional space can be computed from corresponding points in the images of two cameras which are calibrated in a global coordinate system. The calibration procedure and the localization, as well as some aspects of the measurement accuracy achieved, are discussed. Real-world applications provide an uncertain static or dynamic environment which complicates the tracking of a target. To overcome problems due to noisy data or even failed target detection in image frames, a filtering procedure is applied for estimating the airship's pose. In a first step, points in the two-dimensional image planes are directly tracked and propagated forward to the vehicle pose. In a second step, an adaptive noise Kalman filter is applied for estimating and predicting the flight trajectory. Its state is propagated back to points in the image planes to guide the detection algorithm by defining regions of confidence. Both approaches are combined in a tracking algorithm. In-flight measurements are used to validate the parameters of the adaption procedure. Some experimental results are shown.