Convolutional Neural Networks (CNNs) have been applied to visual tracking with demonstrated success in recent years. Most CNN-based trackers utilize hierarchical features extracted from a certain layer to represent the target. However, features from a certain layer are not always effective for distinguishing the target object from the backgrounds especially in the presence of complicated interfering factors (e.g., heavy occlusion, background clutter, illumination variation, and shape deformation). In this work, we propose a CNN-based tracking algorithm which hedges deep features from different CNN layers to better distinguish target objects and background clutters. Correlation filters are applied to feature maps of each CNN layer to construct a weak tracker, and all weak trackers are hedged into a strong one. For robust visual tracking, we propose a hedge method to adaptively determine weights of weak classifiers by considering both the difference between the historical as well as instantaneous performance, and the difference among all weak trackers over time. In addition, we design a Siamese network to define the loss of each weak tracker for the proposed hedge method. Extensive experiments on large benchmark datasets demonstrate the effectiveness of the proposed algorithm against the state-of-the-art tracking methods.

中文翻译：

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对视觉跟踪的深层功能进行套期保值。

卷积神经网络（CNN）已应用于视觉跟踪，并在近年来取得了成功。大多数基于CNN的跟踪器都利用从某一层提取的分层特征来表示目标。但是，特定层的特征并不总是有效地将目标对象与背景区分开，特别是在存在复杂干扰因素（例如，严重遮挡，背景杂波，照明变化和形状变形）的情况下。在这项工作中，我们提出了一种基于CNN的跟踪算法，该算法对冲来自不同CNN层的深层特征，以更好地区分目标对象和背景杂波。将相关滤波器应用于每个CNN图层的特征图，以构造一个弱跟踪器，并将所有弱跟踪器套期为一个强跟踪器。为了进行强大的视觉跟踪，我们提出一种套期保值方法，通过考虑历史性能和即时性能之间的差异以及所有弱跟踪器之间随时间的差异来自适应地确定弱分类器的权重。此外，我们设计了一个暹罗网络来为所建议的套期保值方法定义每个弱跟踪器的损失。在大型基准数据集上进行的大量实验证明了该算法针对最新跟踪方法的有效性。