Global Context Attention for Robust Visual Tracking

doi:10.3390/s23052695

2023-03-01

Janghoon Choi *

DOI：10.3390/s23052695

原文链接

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摘要

En 中文

尽管最近在基于连体网络的视觉跟踪方法中取得了进展，其中它们在许多大规模视觉跟踪基准上显示出高性能指标，但是关于与目标对象具有相似外观的干扰对象的持续挑战仍然存在。为了解决上述问题，我们提出了一种用于视觉跟踪的新颖的全局上下文注意模块，其中所提出的模块可以提取和总结整体全局场景信息以调制目标嵌入以提高可辨别力和鲁棒性。我们的全局上下文注意力模块接收全局特征相关图以从给定场景引出上下文信息，并生成通道和空间注意力权重以调制目标嵌入以聚焦于目标对象的相关特征通道和空间部分。我们提出的跟踪算法在大规模视觉跟踪数据集上进行了测试，与基线跟踪算法相比，我们显示出改进的性能，同时以实时速度实现了竞争性能。额外的消融实验也验证了所提出的模块的有效性，其中我们的跟踪算法显示了视觉跟踪的各种挑战性属性的改进。

Keyword:

visual tracking

object tracking

attention models

model-free tracking

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Sensors

IF:

3.5

论文数:

7.2W

被引数:

20.9W

机构

kyungpook national university (knu)

学者数:

1.8W

论文数: 1.8W

被引数: 14

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