DCFG: Diverse Cross-Channel Fine-Grained Feature Learning and Progressive Fusion Siamese Tracker for Thermal Infrared Target Tracking

• URL: http://arxiv.org/abs/2504.14311v1
• Date: Sat, 19 Apr 2025 14:24:37 GMT
• Title: DCFG: Diverse Cross-Channel Fine-Grained Feature Learning and Progressive Fusion Siamese Tracker for Thermal Infrared Target Tracking
• Authors: Ruoyan Xiong, Yuke Hou, Princess Retor Torboh, Hui He, Huanbin Zhang, Yue Zhang, Yanpin Wang, Huipan Guan, Shang Zhang,
• Abstract summary: Cross-channel fine-grained feature learning network to suppress dominant target features.<n>Channel rearrangement mechanism to enhance efficient in-formation flow.<n> specialized cross-channel fine-grained loss function to guide feature groups toward distinct discriminative re-gions of the target.
• Score: 11.3097285242147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To address the challenge of capturing highly discriminative features in ther-mal infrared (TIR) tracking, we propose a novel Siamese tracker based on cross-channel fine-grained feature learning and progressive fusion. First, we introduce a cross-channel fine-grained feature learning network that employs masks and suppression coefficients to suppress dominant target features, en-abling the tracker to capture more detailed and subtle information. The net-work employs a channel rearrangement mechanism to enhance efficient in-formation flow, coupled with channel equalization to reduce parameter count. Additionally, we incorporate layer-by-layer combination units for ef-fective feature extraction and fusion, thereby minimizing parameter redun-dancy and computational complexity. The network further employs feature redirection and channel shuffling strategies to better integrate fine-grained details. Second, we propose a specialized cross-channel fine-grained loss function designed to guide feature groups toward distinct discriminative re-gions of the target, thus improving overall target representation. This loss function includes an inter-channel loss term that promotes orthogonality be-tween channels, maximizing feature diversity and facilitating finer detail capture. Extensive experiments demonstrate that our proposed tracker achieves the highest accuracy, scoring 0.81 on the VOT-TIR 2015 and 0.78 on the VOT-TIR 2017 benchmark, while also outperforming other methods across all evaluation metrics on the LSOTB-TIR and PTB-TIR benchmarks.

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