In recent years, the background-aware correlation filters have achie-ved a lot of research interest in the visual target tracking. However, these methods cannot suitably model the target appearance due to the exploitation of hand-crafted features. On the other hand, the recent deep learning-based visual tracking methods have provided a competitive performance along with extensive computations. In this paper, an adaptive background-aware correlation filter-based tracker is proposed that effectively models the target appearance by using either the histogram of oriented gradients (HOG) or convolutional neural network (CNN) feature maps. The proposed method exploits the fast 2D non-maximum suppression (NMS) algorithm and the semantic information comparison to detect challenging situations. When the HOG-based response map is not reliable, or the context region has a low semantic similarity with prior regions, the proposed method constructs the CNN context model to improve the target region estimation. Furthermore, the rejection option allows the proposed method to update the CNN context model only on valid regions. Comprehensive experimental results demonstrate that the proposed adaptive method clearly outperforms the accuracy and robustness of visual target tracking compared to the state-of-the-art methods on the OTB-50, OTB-100, TC-128, UAV-123, and VOT-2015 datasets.

中文翻译：

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超越背景感知相关滤波器：通过手工制作的自适应上下文建模和用于视觉跟踪的深度 RGB 特征

近年来，背景感知相关滤波器在视觉目标跟踪方面取得了很多研究兴趣。然而，由于利用了手工制作的特征，这些方法不能适当地对目标外观进行建模。另一方面，最近基于深度学习的视觉跟踪方法提供了具有竞争力的性能以及广泛的计算。在本文中，提出了一种基于自适应背景感知相关滤波器的跟踪器，它通过使用定向梯度直方图 (HOG) 或卷积神经网络 (CNN) 特征图有效地对目标外观进行建模。所提出的方法利用快速二维非极大值抑制 (NMS) 算法和语义信息比较来检测具有挑战性的情况。当基于 HOG 的响应图不可靠时，或者上下文区域与先验区域的语义相似度较低，该方法构建CNN上下文模型以改进目标区域估计。此外，拒绝选项允许所提出的方法仅在有效区域上更新 CNN 上下文模型。综合实验结果表明，与 OTB-50、OTB-100、TC-128、UAV-123 和 VOT 上的最新方法相比，所提出的自适应方法明显优于视觉目标跟踪的准确性和鲁棒性-2015 数据集。