National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China.
Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China.
Sensors (Basel). 2021 Sep 24;21(19):6388. doi: 10.3390/s21196388.
Visual tracking task is divided into classification and regression tasks, and manifold features are introduced to improve the performance of the tracker. Although the previous anchor-based tracker has achieved superior tracking performance, the anchor-based tracker not only needs to set parameters manually but also ignores the influence of the geometric characteristics of the object on the tracker performance. In this paper, we propose a novel Siamese network framework with ResNet50 as the backbone, which is an anchor-free tracker based on manifold features. The network design is simple and easy to understand, which not only considers the influence of geometric features on the target tracking performance but also reduces the calculation of parameters and improves the target tracking performance. In the experiment, we compared our tracker with the most advanced public benchmarks and obtained a state-of-the-art performance.
视觉跟踪任务分为分类任务和回归任务，并引入流形特征来提高跟踪器的性能。虽然以前的基于锚点的跟踪器已经取得了优异的跟踪性能，但基于锚点的跟踪器不仅需要手动设置参数，而且还忽略了物体的几何特征对跟踪器性能的影响。在本文中，我们提出了一种基于 ResNet50 作为骨干网络的新型孪生网络框架，这是一种基于流形特征的无锚点跟踪器。该网络设计简单易懂，不仅考虑了几何特征对目标跟踪性能的影响，还减少了参数的计算，提高了目标跟踪性能。在实验中，我们将我们的跟踪器与最先进的公共基准进行了比较，获得了最先进的性能。
