Under the influence of deep learning, many trackers have emerged recently. Among them, Siamese network reaches a pleasant balance between accuracy and speed, but its tracking performance still lags behind other trackers. In this paper, we have proposed a Hierarchical Correlation Siamese Network (HC-Siam) for object tracking. The tracker uses convolutional features of each layer to compare the correlation and identifies the position of the tracking object depending on the greatest correlation. Meanwhile, we have designed a Correlation Attention Module (CA-Module). For various objects, this module can assign different weights to the hierarchical correlation and help the network choose the distinct correlation from the hierarchical correlation. Besides, objects’ size and scale constantly varied during tracking, we claimed to use the separate scale factor in the wide and high directions to decrease the deformation of bounding boxes and increase the accuracy of our tracker. On the OTB dataset, the accuracy of HC-Siam is 6.5% higher than the baseline, and the speed of our tracker can reach 85 fps. On the VOT dataset, HC-Siam also has better performance in speed and accuracy.

在深度学习的影响下，最近出现了许多跟踪器。其中，暹罗网络在准确性和速度之间达到了令人满意的平衡，但其跟踪性能仍然落后于其他跟踪器。在本文中，我们提出了一种用于对象跟踪的层次相关暹罗网络（HC-Siam）。跟踪器使用每一层的卷积特征来比较相关性，并根据最大相关性来标识跟踪对象的位置。同时，我们设计了一个相关注意模块（CA-Module）。对于各种对象，此模块可以为分层相关性分配不同的权重，并帮助网络从分层相关性中选择不同的相关性。此外，物体的大小和比例在跟踪过程中会不断变化，我们声称在宽方向和高方向使用单独的比例因子来减少边界框的变形并提高跟踪器的精度。在OTB数据集上，HC-Siam的准确性比基线高6.5％，我们的跟踪器的速度可以达到85 fps。在VOT数据集上，HC-Siam在速度和准确性上也具有更好的性能。