SiamATTRPN: Enhance Visual Tracking With Channel and Spatial Attention

Visual tracking is an important research topic in the field of computer vision. The current Siamese tracker based on the region proposal network (SiamRPN) has achieved promising tracking results in terms of efficiency and performance. However, through our empirical study, we have observed that deep features learned by SiamRPN are of substandard quality, as the salient regions within the deep features fail to correspond accurately with meaningful objects. To address this limitation, we propose an approach to enhance the quality of the learned deep features through the incorporation of an attention mechanism. Attention mechanisms have been shown to be effective in distinguishing similar objects, as they suppress background objects while highlighting target information that is most relevant. As a result, a new tracking method with channel and spatial attention termed SiamATTRPN is explored. To verify the effectiveness of SiamATTRPN, experiments on benchmark datasets demonstrate that our proposed tracker outperforms the baseline tracker significantly.