Learning Feature Interaction Alignment Network for Siamese Region Proposal Visual Tracking

Luyao Liu

doi:10.1109/LSP.2025.3582891

2025-01-01

PRE

Luyao Liu

Peng Gao

DOI：10.1109/LSP.2025.3582891

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摘要

En 中文

现有的基于Siamese区域提议网络的视觉跟踪器通过比较目标模板和搜索区域的特征来确定目标的位置和大小。然而，由于尺度变化和外观变化，可能导致特征不一致，从而影响跟踪性能。为解决此问题，我们创新性地引入一个特征交互对齐网络，以学习目标模板与搜索区域之间的特征交互。这使跟踪器能够在复杂场景中更好地对齐目标特定特征。通过特征交互，跟踪器能够在更精细的层次上执行特征融合。此外，为使现有跟踪器学习特征交互，我们改进了损失函数，以更好地适应特征交互对齐的需求。优化后的损失函数能更有效地平衡不同类型的预测误差，增强了模型对复杂场景的适应性。在四个公开基准数据集上的实验结果表明，所提出的特征交互对齐网络提高了基准跟踪器的准确性和鲁棒性，为基于Siamese区域提议网络的现有视觉跟踪方法的改进提供了新的方向。

Keyword:

Visual tracking

image processing

feature enhancement

deep learning

期刊

IEEE Signal Processing Magazine

IF:

9.6

论文数:

1.1W

被引数:

1.7W

机构

Qufu Normal University

学者数:

7.8K

论文数: 5.8K

被引数: 5.4K

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