Vision based ground target tracking for rotor UAV

This paper studies an efficient ground target tracking algorithm for rotor Unmanned Aerial Vehicle (UAV) to overcome the contradiction among the target tracking rapidity, precision and robustness for aerial vehicle. Firstly, Scale Invariant Feature Transform (SIFT) algorithm, which has a better robust performance during rotation, scaling and changes of illumination, is utilized to extract and match the feature points in order to realize target recognition and positioning. Secondly, using top-down tracking method, Kalman filter is combined to estimate the target position in the next frame and search target in the predicted area, it can avoid blind matching, improve tracking rapidity and reduce the ratio of losing target. Finally, an experimental platform of rotor UAV visual tracking is set up and the ground target tracking algorithm is tested. The experiment results show that the algorithm can achieve ground target tracking effectively and has good real-time performance and robustness.