An Enhanced Siamese Network-Based Visual Tracking Algorithm with a Dual Attention Mechanism

Abstract

Aiming at the problems of SiamFC, such as shallow network architecture, a fixed template, a lack of semantic understanding, and temporal modeling, this paper proposes a robust target-tracking algorithm that incorporates both channel and spatial attention mechanisms. The backbone network of our algorithm adopts depthwise, separable convolution to improve computational efficiency, adjusts the output stride and convolution kernel size to improve the network feature extraction capability, and optimizes the network structure through neural architecture search, enabling the extraction of deeper, richer features with stronger semantic information. In addition, we add channel attention to the target template branch after feature extraction to make it adaptively adjust the weights of different feature channels. In the search region branch, a sequential combination of channel and spatial attention is introduced to model spatial dependencies among pixels and suppress background and distractor information. Finally, we evaluate the proposed algorithm on the OTB2015, VOT2018, and VOT2016 datasets. The results show that our method achieves a tracking precision of 0.631 and a success rate of 0.468, improving upon the original SiamFC by 3.4% and 1.2%, respectively. The algorithm ensures robust tracking in complex scenarios, maintains real-time performance, and further reduces both parameter counts and overall computational complexity.