Occlusion and objects with the same appearance as the target (known as distractors) are extremely challenging in the tracking domain, and distractors appearing around the target during occlusion tend to cause tracking distractors. To resolve this problem, improving the tracking performance under conditions of serious occlusions and distractor by exploiting the context auxiliary feature was proposed. First, the context strength feature and distractors around the target were detected. Second, a dynamic model that can describe the movements of context strength feature and target well and the constraint of distractor were built. Finally, the dynamic model of context strength feature and target were described in particle filter, and the T-S tracking algorithm was proposed. Using a challenging test video from the SPEVI and OTB100 datasets, the proposed algorithm was compared with other six highly ranked tracking algorithms. Two types of evaluation were adopted during testing. The experimental results demonstrate that the error pixel is 24 pixel and 8 pixel when T-S algorithm tracking multifaces and infrared car from SPEVI datasheet, when tracking eight testing videos from OTB100 dataset, the success rate of T-S algorithm is 0.51 when overlap threshold is 0.5 and surpass other compared algorithm. Our proposed T-S algorithm performs well when tracking targets under conditions of serious occlusions and distractors.