Visual attention has recently achieved great success and wide application in deep neural networks. Existing methods based on Siamese network have achieved a good accuracy–efficiency trade-off in visual tracking. However, the training time of Siamese trackers becomes longer for the deeper network and larger training data. Further, Siamese trackers cannot predict the target location well in fast motion, full occlusion, camera motion, and similar object scenarios. Due to these difficulties, we develop an end-to-end Siamese attention network for visual tracking. Our approach is to introduce an attention branch in the region proposal network that contains a classification branch and a regression branch. We perform foreground–background classification by combining the scores of the classification branch and the attention branch. The regression branch predicts the bounding boxes of the candidate regions based on the classification results. Furthermore, the proposed tracker achieves the experimental results comparable to the state-of-the-art tracker on six tracking benchmarks. In particular, the proposed method achieves an AUC score of 0.503 on LaSOT, while running at 40 frames per second (FPS).

视觉注意力最近在深度神经网络中获得了巨大的成功并得到了广泛的应用。现有的基于暹罗网络的方法已经在视觉跟踪中实现了良好的精度与效率之间的权衡。但是，对于更深的网络和更大的训练数据，暹罗跟踪器的训练时间变得更长。此外，在快速运动，完全遮挡，摄像机运动和类似物体场景中，暹罗跟踪器无法很好地预测目标位置。由于这些困难，我们开发了一个用于视觉跟踪的端到端暹罗注意力网络。我们的方法是在区域提议网络中引入一个关注分支，该分支包含一个分类分支和一个回归分支。我们通过组合分类分支和注意力分支的分数来执行前景-背景分类。回归分支根据分类结果预测候选区域的边界框。此外，在六个跟踪基准上，所提出的跟踪器获得了与最新跟踪器相当的实验结果。特别是，提出的方法在LaSOT上达到AUC得分0.503，同时以每秒40帧（FPS）的速度运行。