With the development of deep learning, trackers based on convolutional neural networks (CNNs) have made significant achievements in visual tracking over the years. The fully connected Siamese network (SiamFC) is a typical representation of those trackers. SiamFC designs a two-branch architecture of a CNN and models’ visual tracking as a general similarity-learning problem. However, the feature maps it uses for visual tracking are only from the last layer of the CNN. Those features contain high-level semantic information but lack sufficiently detailed texture information. This means that the SiamFC tracker tends to drift when there are other same-category objects or when the contrast between the target and the background is very low. Focusing on addressing this problem, we design a novel tracking algorithm that combines a correlation filter tracker and the SiamFC tracker into one framework. In this framework, the correlation filter tracker can use the Histograms of Oriented Gradients (HOG) and color name (CN) features to guide the SiamFC tracker. This framework also contains an evaluation criterion which we design to evaluate the tracking result of the two trackers. If this criterion finds the SiamFC tracker fails in some cases, our framework will use the tracking result from the correlation filter tracker to correct the SiamFC. In this way, the defects of SiamFC’s high-level semantic features are remedied by the HOG and CN features. So, our algorithm provides a framework which combines two trackers together and makes them complement each other in visual tracking. And to the best of our knowledge, our algorithm is also the first one which designs an evaluation criterion using correlation filter and zero padding to evaluate the tracking result. Comprehensive experiments are conducted on the Online Tracking Benchmark (OTB), Temple Color (TC128), Benchmark for UAV Tracking (UAV-123), and Visual Object Tracking (VOT) Benchmark. The results show that our algorithm achieves quite a competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.

中文翻译：

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MFCFSiam：具有用于视觉跟踪的多功能功能的相关过滤器引导的连体网络

随着深度学习的发展，基于卷积神经网络（CNN）的跟踪器多年来在视觉跟踪方面取得了重大成就。完全连接的暹罗网络（SiamFC）是这些跟踪器的典型代表。SiamFC设计了CNN的两分支架构，并将视觉跟踪建模为一般的相似性学习问题。但是，它用于视觉跟踪的要素图仅来自CNN的最后一层。这些功能包含高级语义信息，但缺少足够详细的纹理信息。这意味着当存在其他相同类别的对象或目标与背景之间的对比度非常低时，SiamFC跟踪器会发生漂移。专注于解决这个问题，我们设计了一种新颖的跟踪算法，将相关滤波器跟踪器和SiamFC跟踪器组合到一个框架中。在此框架中，相关过滤器跟踪器可以使用“定向直方图”（HOG）和“颜色名称”（CN）功能来指导SiamFC跟踪器。该框架还包含一个评估标准，我们设计该评估标准来评估两个跟踪器的跟踪结果。如果此标准在某些情况下发现SiamFC跟踪器失败，我们的框架将使用相关过滤器跟踪器的跟踪结果来校正SiamFC。这样，通过HOG和CN功能可以弥补SiamFC的高级语义功能的缺陷。因此，我们的算法提供了一个框架，该框架将两个跟踪器组合在一起，并使它们在视觉跟踪中相互补充。据我们所知，我们的算法也是第一个使用相关滤波器和零填充设计评估标准来评估跟踪结果的算法。在在线跟踪基准（OTB），寺庙颜色（TC128），无人机跟踪基准（UAV-123）和视觉对象跟踪（VOT）基准上进行了全面的实验。结果表明，与基线跟踪器和其他几种最新的跟踪器相比，我们的算法具有相当好的竞争性能。