ATrans: Improving single object tracking based on dual attention

The current mainstream Siamese-based object tracking methods usually match the local regions of two video frames. This regional association method ignores the global features of object modeling. To solve the robustness of long-term object tracking and improve the efficiency of object tracking to a certain extent, we propose a new tracking framework based on the dual attention mechanism, named ATrans. Our core design is based on the flexibility of the attention mechanism. We propose a dual attention module to obtain more precise features and enhance the robustness of feature extraction by paying attention to contextual information. We construct our ATrans tracking framework by stacking multiple encoders with dual attention modules and a decoder and placing a localization head on top. In addition, to solve the drift problem in the long-term object tracking process, we add an online update mechanism to the encoder structure to dynamically update the target template to enhance the robustness of the long-term tracking process. At the same time, to further improve the efficiency of the model, we propose a background removal module to reduce the amount of computation by discarding unnecessary background areas during the object tracking process. Experiments show that our tracker performs well on large datasets such as Lasot, Got10k, and TrackingNet.