Bidirectional Consistency Constrained Template Update Learning for Siamese Trackers

Kexin Chen, Xue Zhou, Chao Liang, Jianxiao Zou
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Abstract

This paper presents an online template update method with bidirectional consistency constraint for Siamese trackers. Due to continuously applying cross-correlation mechanism between template and the search region, the performance of Siamese trackers highly relies on the fidelity of template. Therefore, besides standard linear update, learning the template update methods attract attention. Inspired by this, in this paper we adopt a learning to update model called UpdateNet as our baseline. Different from it, we further propose a novel bi-directional consistency loss as a constraint to learn the template update more smoothly and stably. Our method considers both forward and backward information for each medium frame, thus introducing a multi-stage bidirectional simulated tracking training mechanism. We apply our model to a Siamese tracker, SiamRPN and demonstrate the effectiveness and robustness of our proposed method compared with traditional UpdateNet in the Large-scale Single Object Tracking (LaSOT) dataset.
Siamese跟踪器的双向一致性约束模板更新学习
提出了一种基于双向一致性约束的连体跟踪器在线模板更新方法。由于模板与搜索区域之间的相互关联机制不断被应用,因此Siamese跟踪器的性能高度依赖于模板的保真度。因此,除了标准的线性更新之外,学习模板更新方法也备受关注。受此启发,在本文中,我们采用了一个名为UpdateNet的学习更新模型作为我们的基线。与之不同的是,我们进一步提出了一种新的双向一致性损失作为约束来学习模板更新更平稳。该方法同时考虑了每个中间帧的前向和后向信息,从而引入了一种多阶段双向模拟跟踪训练机制。我们将我们的模型应用于SiamRPN,并在大规模单目标跟踪(LaSOT)数据集中与传统的UpdateNet相比,证明了我们提出的方法的有效性和鲁棒性。
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