Two-stage aware attentional Siamese network for visual tracking

Highlights：

• 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.