CoSiNet: Dual-Branch Collaborative Siamese Network for Visual Object Tracking
Wenjun Zhou, Yao Liu, Nan Wang, Yifan Wang, Bo Peng · 2024
This article presents a dual-branch Collaborative Siamese network architecture designed for visual object tracking, which we refer to as CoSiNet. The dual-branch collaborative Siamese network comprises two network branches: a shallow branch, which focuses on target localization to enhance resistance to interference from objects with similar characteristics, and a deep branch, which emphasizes the extraction of more abstract semantic information related to the object. Furthermore, we have devised a Channel Attention Feature Enhancement Module and a Spatial Channel Attention Feature Enhancement Module to augment feature extraction while mitigating the influence of background noise. In the concluding stages, an Adaptive Fusion Module is employed to amalgamate the response maps from both branches, resulting in an enhanced final response map. Experimental results, conducted on two publicly available datasets, demonstrate that our algorithm outperforms other state-of-the-art techniques in terms of tracking performance.