The fully convolutional siamese network based trackers achieve great progress recently. Most of these methods focus on improving the capability of siamese network to represent the target. In this paper, we propose our model which focuses on estimating the state of the target with our proposed novel IoU (intersection over union) loss function which is named AIoU. Our model consists of a siamese subnetwork for feature extraction and a target estimation subnetwork for state representation. The target estimation subnetwork contains a classification head for classifying background and foreground and a regression head for estimating target. In order to regress better bounding boxes, we further study the loss function utilized in the regression head and propose a powerful IoU loss function. Our tracker achieves competitive performance on OTB2015, VOT2018, and VOT2019 benchmarks with a speed of 180 FPS, which proves the effectiveness of our method.

基于全卷积暹罗网络的跟踪器最近取得了长足的进步。这些方法大多数都集中在提高暹罗网络代表目标的能力上。在本文中，我们提出了我们的模型，该模型着重于利用我们提出的新颖的IoU（联合交叉）损失函数（称为AIoU）来估计目标的状态。我们的模型由用于特征提取的暹罗子网络和用于状态表示的目标估计子网络组成。目标估计子网包含用于对背景和前景进行分类的分类头和用于估计目标的回归头。为了回归更好的边界框，我们进一步研究了回归头中使用的损失函数，并提出了强大的IoU损失函数。我们的跟踪器在OTB2015，VOT2018，