Integrated channel attention method for Siamese tracker
Ziyi Zhou, Yingran Jin, Yun Gao · 2024
It is critical to make full use of the information of the backbone to improve the performance of object tracking. A common way to mine useful information is to add attention to features. However, most trackers only use single attention to mine the features and fail to utilize the effective information in the backbone. To leverage the useful information of features in multiple ways, this paper proposes an integrated channel attention mechanism based on all kinds of commonly used channel attention methods. First, we used ResNet50 as the backbone, and then we used four attention methods to process the fourth-stage features that were extracted from the backbone to obtain attention factors. Then, through adaptive weighting, we added the four attention methods to the original features. It adaptively adjusts the importance of each channel attention, suppresses redundant information, and better captures key features of the tracked object in different channels. The effectiveness of our approach is validated on five tracking benchmarks.