Numerous trackers have been proposed in recent years with considerable success. But few trackers can cope with all scenarios without failures. It is very difficult to design a tracker robust enough to keep off tracking failure. As failure is inevitable, we propose a framework to correct tracker, verify failure, predict object position and re-detect object. The original model of the first frame is used to correct the tracker. Then, the confidence of tracking is used to verify tracking failure. Finally, the spatial context is used to predict the object location when the object is invisible and re-detect object after tracking failure. A variety of comparative experiments validate the superiority of our tracker in comparison against other trackers.