xAerial object tracking technology is widely used in unmanned aerial vehicle, air traffic control and drone counters. The tracking algorithm using only a single feature is difficult to adapt to the complex tracking environment. How to achieve robust tracking of aerial objects under different interference conditions is the main problem studied in this paper. To solve this problem, this paper proposed a multi-feature fusion tracking algorithm based on confidence evaluation. At the same time, in order to solve the problem of large variation of aerial object scale, a parallel scale estimation strategy based on the Discriminative Scale Space Tracking algorithm is proposed in this paper. This method estimates the optimal width and height of the object respectively, which can estimate the scale variation of aerial object more stably. For the occlusion problem, an occlusion-aware part-based model is proposed in this paper. The part-based local model can deal with the occlusion problem effectively, while the global model is more suitable for dealing with the fast motion and motion blur of the object. Therefore, a tracking method based on multi-tracker relay is proposed in this paper. In this method, the tracking state is judged according to the confidence of the model. In the normal tracking state, the global model based on multi-feature fusion is used, and when occlusion interference exists, the tracking model is replaced by the parts-based local model. In this way, the algorithm can effectively deal with various tracking situations.