Visual object tracking remains a challenge facing an intelligent control system. A variety of applications serve many purposes such as surveillance. The developed technology faces plenty of obstacles that should be addressed including occlusion. In visual tracking, online learning techniques are most common due to their efficiency for most video sequences. Many object tracking techniques have emerged. However, the drifting problem in the case of noisy updates has been a stumbling block for the majority of relevant techniques. Such a problem can now be surmounted through updating the classifiers. The proposed system is called the Occluded Object Tracking System (OOTS) It is a hybrid system constructed from two algorithms: a fast technique Circulant Structure Kernels with Color Names (CSK-CN) and an efficient algorithm occlusion-aware Real-time Object Tracking (ROT). The proposed OOTS is evaluated with standard visual tracking benchmark databases. The experimental results proved that the proposed OOTS system is more reliable and provides efficient tracking results than other compared methods.