Zhu Chengfei, Jiang Shan, Li Shuxiao, Lan Xiaosong
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2021 Jan 25;21(3):790. doi: 10.3390/s21030790.
Visual tracking is a basic task in many applications. However, the heavy computation and low speed of many recent trackers limit their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter-based trackers restricts their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter-based trackers and propose an efficient and adaptive training sample update scheme. The training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with a difference hashing algorithm or discarded according to tracking result reliability. In addition, we expand our new tracker to long-term tracking. On the basis of the proposed model updating mechanism, we propose a new tracking state discrimination mechanism to accurately judge tracking failure, and resume tracking after the target is recovered. Experiments on OTB-2015, Temple Color 128 and UAV123 (including UAV20L) demonstrate that our tracker performs favorably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2 GHz).
视觉跟踪是许多应用中的一项基本任务。然而，许多近期跟踪器的繁重计算量和低速度限制了它们在一些计算能力受限场景中的应用。另一方面，大多数基于相关滤波器的跟踪器简单的更新方案限制了它们在目标变形和遮挡期间的鲁棒性。在本文中，我们探索了基于相关滤波器的跟踪器的更新方案，并提出了一种高效且自适应的训练样本更新方案。根据使用差异哈希算法测量的与现有样本之间的距离，将每一帧中提取的训练样本更新到训练集中，或者根据跟踪结果的可靠性将其丢弃。此外，我们将我们的新跟踪器扩展到长期跟踪。在提出的模型更新机制的基础上，我们提出了一种新的跟踪状态判别机制，以准确判断跟踪失败，并在目标恢复后恢复跟踪。在OTB - 2015、Temple Color 128和UAV123（包括UAV20L）上的实验表明，我们的跟踪器在计算量小的情况下优于现有跟踪器，并且在配备英特尔i7 - 8700 CPU（3.2 GHz）的台式计算机上运行速度超过100帧每秒。
