Visual target tracking is crucial for an unmanned aerial vehicle (UAV) to conduct a strike, location, and reconnaissance against moving and time-sensitive ground targets; however it is hindered by imaging platform motion, severe occlusion, and target disappearance from the field of vision. A visual ground target tracking approach based on a motion model for UAVs is proposed to enhance the robustness for these challenges. First, a fast optical flow algorithm based on the dense inverse search is used to compute the homography transformation between two consecutive frames, and the target position is mapped from the historical frame to the present reference frame to decouple the motion of the imaging platform. The target motion on the reference frame is then modeled using a linear motion model, which is used to predict the target position when occlusion occurs. Finally, short-term and long-term trackers are combined to solve the tracking drift generated by the false update of the tracker for partially occluded target samples. Based on the discriminative correlation filter, experiments were conducted on the collected UAV videos. The findings reveal that the proposed approach can substantially improve the adaptation to the imaging platform motion and severe occlusion, and can be easily combined with other target tracking approaches.