To deal with the defects of Compressive Tracking (CT) in the tracking error and the sample collection, firstly, the predictive vector was introduced to search samples that can direct motion of the target. Then the fan-shaped sampling areas reduced the amount of computation greatly. Furthermore, we could determine complex background or occlusion through comparison of the neighboring target images, and then update the classifier parameters automatically by applying the Bhattacharyya coefficient. Experiment shows that these improvements can avoid the failure of compressive tracking and the adaptive predictive compressive tracking (VACT) is better than the original algorithm (CT) in robustness and speed.