Siamese trackers demonstrated high performance in object tracking due to their balance between accuracy and speed. Unlike classification-based CNNs, deep similarity networks are specifically designed to address the image similarity problem and thus are inherently more appropriate for the tracking task. However, Siamese trackers mainly use the last convolutional layers for similarity analysis and target search, which restricts their performance. In this paper, we argue that using a single convolutional layer as feature representation is not an optimal choice in a deep similarity framework. We present a Multiple Features-Siamese Tracker (MFST), a novel tracking algorithm exploiting several hierarchical feature maps for robust tracking. Since convolutional layers provide several abstraction levels in characterizing an object, fusing hierarchical features allows to obtain a richer and more efficient representation of the target. Moreover, we handle the target appearance variations by calibrating the deep features extracted from two different CNN models. Based on this advanced feature representation, our method achieves high tracking accuracy, while outperforming the standard siamese tracker on object tracking benchmarks.The source code and trained models are available at https://github.com/zhenxili96/MFST.

暹罗跟踪器由于其在准确性和速度之间的平衡而在对象跟踪中表现出了很高的性能。与基于分类的CNN不同，深度相似度网络是专门为解决图像相似度问题而设计的，因此本质上更适合于跟踪任务。但是，暹罗跟踪器主要将最后的卷积层用于相似性分析和目标搜索，这限制了它们的性能。在本文中，我们认为在深度相似性框架中使用单个卷积层作为特征表示不是最佳选择。我们提出了一种多功能连体跟踪器（MFST），这是一种新颖的跟踪算法，它利用了几个分层的特征图来进行稳健的跟踪。由于卷积层在表征对象时提供了几种抽象级别，融合分层功能可以获取更丰富，更有效的目标表示。此外，我们通过校准从两个不同的CNN模型提取的深度特征来处理目标外观变化。基于这种先进的功能表示，我们的方法在对象跟踪基准方面优于标准的暹罗跟踪器，可实现较高的跟踪精度.https：//github.com/zhenxili96/MFST上提供了源代码和经过训练的模型。