In real-world outdoor video, moving targets such as vehicles and people may be partially or fully occluded by background objects such as buildings and trees, which makes tracking them continuously a very challenging task. In the present work, we present a system to address the problem of tracking targets through occlusions in a motion-based target detection and tracking framework. For an existing track that is fully occluded, a Kalman filter is applied to predict the target-s current position based upon its previous locations. However, the prediction may drift from the target-s true trajectory due to accumulated prediction errors, especially when the occlusion is of long duration. To address this problem, tracks that have disappeared are checked with an extra data association procedure that evaluates the potential association between the track and the new detections, which could be a previously tracked target that is just coming out of occlusion. Another issue that arises with motionbased tracking is that the algorithm may consider the visible part of a partially occluded target as the entire target region. This is problematic because an inaccurate target motion trajectory model will be built, causing the Kalman filter to generate inaccurate target position predictions, which can yield a divergence between the track and the true target trajectory. Accordingly, we present a method that provides reasonable estimates of the partially-occluded target centers. Experimental results conducted on real-world unmanned air vehicle (UAV) video sequences demonstrate that the proposed system significantly improves the track continuity in various occlusion scenarios.