FGSGT: Saliency-Guided Siamese Network Tracker Based on Key Fine-Grained Feature Information for Thermal Infrared Target Tracking

• URL: http://arxiv.org/abs/2504.14309v1
• Date: Sat, 19 Apr 2025 14:13:15 GMT
• Title: FGSGT: Saliency-Guided Siamese Network Tracker Based on Key Fine-Grained Feature Information for Thermal Infrared Target Tracking
• Authors: Ruoyan Xiong, Huanbin Zhang, Shentao Wang, Hui He, Yuke Hou, Yue Zhang, Yujie Cui, Huipan Guan, Shang Zhang,
• Abstract summary: We propose a novel saliency-guided Siamese network tracker based on key fine-grained feature infor-mation.<n>This design captures essential global features from shallow layers, enhances feature diversity, and minimizes the loss of fine-grained in-formation.<n>Experiment results demonstrate that the pro-posed tracker achieves the highest precision and success rates.
• Score: 11.599952876425736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Thermal infrared (TIR) images typically lack detailed features and have low contrast, making it challenging for conventional feature extraction models to capture discriminative target characteristics. As a result, trackers are often affected by interference from visually similar objects and are susceptible to tracking drift. To address these challenges, we propose a novel saliency-guided Siamese network tracker based on key fine-grained feature infor-mation. First, we introduce a fine-grained feature parallel learning convolu-tional block with a dual-stream architecture and convolutional kernels of varying sizes. This design captures essential global features from shallow layers, enhances feature diversity, and minimizes the loss of fine-grained in-formation typically encountered in residual connections. In addition, we propose a multi-layer fine-grained feature fusion module that uses bilinear matrix multiplication to effectively integrate features across both deep and shallow layers. Next, we introduce a Siamese residual refinement block that corrects saliency map prediction errors using residual learning. Combined with deep supervision, this mechanism progressively refines predictions, ap-plying supervision at each recursive step to ensure consistent improvements in accuracy. Finally, we present a saliency loss function to constrain the sali-ency predictions, directing the network to focus on highly discriminative fi-ne-grained features. Extensive experiment results demonstrate that the pro-posed tracker achieves the highest precision and success rates on the PTB-TIR and LSOTB-TIR benchmarks. It also achieves a top accuracy of 0.78 on the VOT-TIR 2015 benchmark and 0.75 on the VOT-TIR 2017 benchmark.

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