SFFN: Serial Feature Fusion Network for Visual Tracking
Qi Kou, Peng Wang, Mengyu Sun, Xiaoyan Li, Ruohai Di, Zhigang Lv · Journal of Physics Conference Series · 2024
Abstract Recently, The Transformer tracker has excellent visual target tracking performance. The parallel arrangement of structural feature fusion networks proposed by the TransT tracker makes the Transformer learn the erroneous information. To address the issue, this paper proposes the SFFN tracker. It constructed a serial feature fusion network with the Transformer module in a serial interleaved arrangement to avoid learning erroneous information during the Transformer feature fusion process. The experiments show that SFFN performs well on the GOT-10k dataset, and it improves over TransT by 2.6%, 2.6%, and 2.3% in AO, SR0.5, and SR0.75, respectively.