Real-time compressive tracking was a simple and effective tracking algorithm. However, there were a number of problems which need to be addressed. First of all, it was easy to introduce errors due to factors such as occlusion and clutter. Secondly, it couldn’t update the positive and negative samples accurately while using fixed tracking window. At last, the number of testing samples was too large, which affected the speed of tracking. The occlusion was checked by comparison between consecutive frames’ histograms, and the coefficient can be also updated adaptively by the comparison result. We searched for more specified areas with multi-scales to find out the best matching place, and to handle scale change of the target on the basis of the original algorithm’s tracking result. The different numbers of sub features sets were utilized to filter the testing samples. In that case, the speed of tracking process would be improved. The strategies we proposed would improve the original algorithm’s performance to avoid the failure of tracking. The experimental results indicate that the algorithm can run in real-time and perform favorably against state-of-the-art algorithms on challenging sequences in terms of efficiency, accuracy and robustness.