Correlation filters are well-known for tracking robustness and accuracy while convolutional neural networks (CNN) is famous for representation learning capability. However, how to combine them to further boost tracking performance remains an open problem. In this paper, we are resolved to derive a more compact double-correlation filter and incorporate an ensemble of double-correlation filters in a membership-based decision fashion where filters are trained on features obtained from different layers of a CNN respectively. The novel double-correlation filter is constructed by maximizing the similarity between the Gaussian-shape label and the correlation of template and training samples, producing a more concise solution that means more computational efficiency. Multiple filters are learned based on multiple-layer CNN features obtained. The final tracking prediction is a membership-weighted decision where membership of each tracker, which is computed according to their performance in previous frames, shows how close a weak tracker-s result is to the truth. Hence our framework not only combines correlation filters and CNN together, but also fully utilizes both deep features providing semantic information to distinguish target from background and their shallow counterparts retaining details beneficial for precise localization. We experiment on benchmark OTB and VOT where our algorithm demonstrates competitive performance versus other state-of-the-art trackers.

相关滤波器以跟踪鲁棒性和准确性而闻名，而卷积神经网络（CNN）以表示学习能力而闻名。但是，如何组合它们以进一步提高跟踪性能仍然是一个悬而未决的问题。在本文中，我们决心导出一个更紧凑的双相关滤波器，并以基于成员资格的决策方式合并一个双相关滤波器的集合，其中分别对从CNN不同层获得的特征进行训练。通过最大化高斯形状标签与模板和训练样本的相关性之间的相似性来构造新颖的双相关滤波器，从而产生更简洁的解决方案，这意味着更高的计算效率。基于获得的多层CNN特征学习多个滤波器。最终的跟踪预测是一种成员资格加权决策，其中根据每个跟踪器在先前帧中的性能来计算每个跟踪器的成员资格，从而表明弱跟踪器的结果与事实的接近程度。因此，我们的框架不仅将相关过滤器和CNN结合在一起，而且还充分利用了提供语义信息以将目标与背景区分开的深层特征，以及保留了有助于精确定位的细节的浅层对应物。我们在基准OTB和VOT上进行了实验，其中我们的算法展示了与其他最新跟踪器相比的竞争性能。因此，我们的框架不仅将相关过滤器和CNN结合在一起，而且还充分利用了提供语义信息以将目标与背景区分开的深层特征，以及保留了有助于精确定位的细节的浅层对应物。我们在基准OTB和VOT上进行了实验，其中我们的算法展示了与其他最新跟踪器相比的竞争性能。因此，我们的框架不仅将相关过滤器和CNN结合在一起，而且还充分利用了提供语义信息以将目标与背景区分开的深层特征，以及保留了有助于精确定位的细节的浅层对应物。我们在基准OTB和VOT上进行了实验，其中我们的算法展示了与其他最新跟踪器相比的竞争性能。