用于视觉跟踪的高置信度模板融合 IoU 引导连体网络

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neurocomputing Pub Date : 2024-10-28 DOI:10.1016/j.neucom.2024.128774
Zhigang Liu , Hao Huang , Hongyu Dong , Fuyuan Xing
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引用次数: 0

摘要

现有的 IoU 引导跟踪器仅在测试阶段使用 IoU 分数作为分类分数的权重,这种训练和测试阶段的模型不匹配会导致跟踪性能低下,尤其是在面对背景干扰时。在本文中,我们提出了一种用于视觉跟踪的高置信度模板融合 IoU 引导暹罗网络(SiamIH)。该网络在训练和测试阶段利用 IoU 信息指导分类,并使跟踪模型抑制背景干扰。为了应对外观变化,我们设计了高置信度模板融合网络,将基于 APCE 的高置信度模板与初始模板融合,生成更可靠的模板。在 OTB2013、OTB2015、UAV123、LaSOT 和 GOT10k 上的实验结果表明,所提出的 SiamIH 实现了最先进的跟踪性能。
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IoU-guided Siamese network with high-confidence template fusion for visual tracking
Existing IoU-guided trackers use IoU score to weight the classification score only in testing phase, this model mismatch between training and testing phases leads to poor tracking performance especially when facing background distractors. In this paper, we propose an IoU-guided Siamese network with High-confidence template fusion (SiamIH) for visual tracking. An IoU-guided distractor suppression network is proposed, which uses IoU information to guide classification in training phase and testing phase, and makes the tracking model to suppress background distractors. To cope with appearance variations, we design a high-confidence template fusion network that fuses APCE-based high-confidence template and the initial template to generate more reliable template. Experimental results on OTB2013, OTB2015, UAV123, LaSOT, and GOT10k demonstrate that the proposed SiamIH achieves state-of-the-art tracking performance.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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