Recently, deep trackers based on the siamese networking are enjoying increasing popularity in the tracking community. Generally, those trackers learn a high-level semantic embedding space for feature representation but lose low-level fine-grained details. Meanwhile, the learned high-level semantic features are not updated during online tracking, which results in tracking drift in presence of target appearance variation and similar distractors. In this paper, we present a novel end-to-end trainable Convolutional Neural Network (CNN) based on the siamese network for distractor-aware tracking. It enhances target appearance representation in both the offline training stage and online tracking stage. In the offline training stage, this network learns both the low-level fine-grained details and high-level coarse-grained semantics simultaneously in a multi-task learning framework. The low-level features with better resolution are complementary to semantic features and able to distinguish the foreground target from background distractors. In the online stage, the learned low-level features are fed into a correlation filter layer and updated in an interpolated manner to encode target appearance variation adaptively. The learned high-level features are fed into a cross-correlation layer without online update. Therefore, the proposed tracker benefits from both the adaptability of the fine-grained correlation filter and the generalization capability of the semantic embedding. Extensive experiments are conducted on the public OTB100 and UAV123 benchmark datasets. Our tracker achieves state-of-the-art performance while running with a real-time frame-rate.

中文翻译：

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具有多任务和动态特征学习的干扰器感知跟踪

最近，基于连体网络的深度跟踪器在跟踪社区中越来越受欢迎。通常，这些跟踪器学习用于特征表示的高级语义嵌入空间，但会丢失低级细粒度细节。同时，学习到的高级语义特征在在线跟踪过程中没有更新，这导致在存在目标外观变化和类似干扰物的情况下跟踪漂移。在本文中，我们提出了一种基于连体网络的新型端到端可训练卷积神经网络 (CNN)，用于干扰感知跟踪。它在离线训练阶段和在线跟踪阶段都增强了目标外观表示。线下训练阶段，该网络在多任务学习框架中同时学习低级细粒度细节和高级粗粒度语义。具有更好分辨率的低级特征与语义特征互补，能够区分前景目标和背景干扰物。在在线阶段，学习到的低级特征被馈送到相关滤波器层并以插值方式更新，以自适应地编码目标外观变化。学习到的高级特征被送入互相关层，无需在线更新。因此，所提出的跟踪器受益于细粒度相关滤波器的适应性和语义嵌入的泛化能力。在公共 OTB100 和 UAV123 基准数据集上进行了广泛的实验。