Recently, Siamese trackers have attracted extensive attention because of their simplicity and low computational cost. However, for most Siamese trackers, only a frame of the video sequence is used as the template, and the template is not updated in inference process, which makes the tracking success rate inferior to the trackers that can update the template online. In the current study, we introduce an enhanced visual attention Siamese network (ESA-Siam). The method is based on a deep convolutional neural network, which integrates channel attention and spatial self-attention to improve the discriminative ability of the tracker for positive and negative samples. Channel attention reflects different targets according to the response value of different channels to achieve better target representation. Spatial self-attention captures the correlation between two arbitrary positions to help locate the target. At the same time, a template search attention module is designed to implicitly update the template features online, which can effectively improve the success rate of the tracker when the target is interfered by the background. The proposed ESA-Siam tracker shows superior performance compared with 18 existing state-of-the-art trackers on five benchmark datasets including OTB50, OTB100, VOT2016, VOT2018, and LaSOT.

中文翻译：

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在线更新模板功能的增强型视觉注意连体网络

最近，暹罗跟踪器因其简单和低计算成本而受到广泛关注。然而，对于大多数Siamese tracker来说，仅使用视频序列的一帧作为模板，并且在推理过程中没有更新模板，这使得跟踪成功率不如可以在线更新模板的tracker。在当前的研究中，我们引入了增强的视觉注意力连体网络（ESA-Siam）。该方法基于深度卷积神经网络，将通道注意力和空间自注意力相结合，提高跟踪器对正负样本的判别能力。通道注意力根据不同通道的响应值反映不同的目标，以达到更好的目标表示。空间自注意力捕获两个任意位置之间的相关性以帮助定位目标。同时设计了模板搜索注意力模块，在线隐式更新模板特征，当目标受到背景干扰时，可以有效提高跟踪器的成功率。在包括 OTB50、OTB100、VOT2016、VOT2018 和 LaSOT 在内的五个基准数据集上，与 18 个现有的最先进跟踪器相比，拟议的 ESA-Siam 跟踪器显示出优越的性能。