Two-stage aware attentional Siamese network for visual tracking

•We propose a novel two-stage aware training framework for siamese networks, in which position-aware and appearance-aware training schemes are presented to optimize the shallow and the deep network layers, respectively. This contribution helps siamese tracker to achieve precise and robust visual tracking.•An effective feature selection module is presented to solve the online adaptation problem of Siamese tracker. By analyzing the changing principle of feature distribution, the module combines diverse attention networks in a unique way to explore the real discriminative features for the current object.•The proposed tracker is evaluated on four popular benchmark datasets extensively. The results demonstrate that the tracker performs better than other state-of-the-art methods in terms of accuracy and robustness.