Dual-stage Feature Attention Enhancement for Siamese Tracker
Yinghua Yuan, Yuli Yang, Yun Tao Gao · 2024
The attention mechanism is an important method for object tracking. However, although some mainstream trackers currently use an attention mechanism to enhance the features extracted from the backbone, they only add attention to the final-stage features without considering the importance of shallow features. To obtain stronger feature representations and promote correlation calculations in the feature fusion network, we enhance the third- and fourth-stage features of the ResNet50 backbone with an attention mechanism and use them as inputs to the Transformer fusion network through direct addition. In this way, the Transformer fusion network can receive stronger feature representations and achieve feature fusion. Our method has been experimentally verified on four mainstream public datasets, and the improved dual-stage feature attention enhancement method can improve the tracking performance of the tracker.