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<p>Siamese-based tracking approaches have demonstrated notable efficacy in visual object tracking recently. Nevertheless, existing Siamese networks suffer from core limitations in their feature extraction and interaction mechanisms: namely, a lack of adaptive modeling capability for multi-scale context and low sensitivity to target instance variations. These shortcomings cause trackers to be prone to drifting when encountering challenges such as occlusion, scale variations, and fast motion. To address this problem, we develop an original dynamic multi-order context fusion siamese network for object tracking, constructing a three-level progressive functional coupling architecture. By introducing the Multi-Order Feature Gated Fusion module, the Adaptive Fine-grained Channel Cross-correlation module, and the Spatial-Channel Coordinated Attention module, we effectively enhance discriminative representation learning, cross-correlation matching accuracy, and target activation. We conducted extensive experiments on four benchmark datasets: OTB100, UAV123, GOT-10K, and LaSOT, to confirm our tracker’s superior performance. Our code is available at: <a href="https://github.com/JSJ515-Group/SiamDMCF">https://github.com/JSJ515-Group/SiamDMCF</a>.</p>

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SiamDMCF: a dynamic multi-order context fusion siamese network for robust visual tracking

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • Yu-e Lin,
  • Xingyuan Ge,
  • Xingzhu Liang,
  • Jinliang Zhang

摘要

Siamese-based tracking approaches have demonstrated notable efficacy in visual object tracking recently. Nevertheless, existing Siamese networks suffer from core limitations in their feature extraction and interaction mechanisms: namely, a lack of adaptive modeling capability for multi-scale context and low sensitivity to target instance variations. These shortcomings cause trackers to be prone to drifting when encountering challenges such as occlusion, scale variations, and fast motion. To address this problem, we develop an original dynamic multi-order context fusion siamese network for object tracking, constructing a three-level progressive functional coupling architecture. By introducing the Multi-Order Feature Gated Fusion module, the Adaptive Fine-grained Channel Cross-correlation module, and the Spatial-Channel Coordinated Attention module, we effectively enhance discriminative representation learning, cross-correlation matching accuracy, and target activation. We conducted extensive experiments on four benchmark datasets: OTB100, UAV123, GOT-10K, and LaSOT, to confirm our tracker’s superior performance. Our code is available at: https://github.com/JSJ515-Group/SiamDMCF.