Abstract
This paper introduces a dual-branch collaborative Siamese network architecture, CoSiNet, for visual object tracking. The network has a shallow branch for precise target localization and a deep branch for extracting rich semantic information. It integrates two specialized modules - Channel Attention and Spatial Channel Attention Feature Enhancement - for improved feature extraction and background noise reduction. An Adaptive Fusion Module combines response maps from both branches to create an enriched final response map. Experimental results show our algorithm outperforms several contemporary techniques on four different public datasets (OTB100, OTB50, DTB70, TColor128), demonstrating the competitiveness of CoSiNet.
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