Mbouombouo Mboungam Abdel Hamid, Zhi Yongfeng, Fonzeu Monguen Cedric Karel
School of Automation, Northwestern Polytechnical University, Xi'an 710002, China.
Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology (KIT), Engesserstr. 20, 76131 Karlsruhe, Germany.
Sensors (Basel). 2024 Oct 13;24(20):6600. doi: 10.3390/s24206600.
The achievement of rapid and reliable image object tracking has long been crucial and challenging for the advancement of image-guided technology. This study investigates real-time object tracking by offering an image target based on nuclear correlation tracking and detection methods to address the challenge of real-time target tracking in complicated environments. In the tracking process, the nuclear-related tracking algorithm can effectively balance the tracking performance and running speed. However, the target tracking process also faces challenges such as model drift, the inability to handle target scale transformation, and target length. In order to propose a solution, this work is organized around the following main points: this study dedicates its first part to the research on kernelized correlation filters (KCFs), encompassing model training, object identification, and a dense sampling strategy based on a circulant matrix. This work developed a scale pyramid searching approach to address the shortcoming that a KCF cannot forecast the target scale. The tracker was expanded in two stages: the first stage output the target's two-dimensional coordinate location, and the second stage created the scale pyramid to identify the optimal target scale. Experiments show that this approach is capable of resolving the target size variation problem. The second part improved the KCF in two ways to meet the demands of a long-term object tracking task. This article introduces the initial object model, which effectively suppresses model drift. Secondly, an object detection module is implemented, and if the tracking module fails, the algorithm is redirected to the object detection module. The target detection module utilizes two detectors, a variance classifier and a KCF. Finally, this work includes trials on object tracking experiments and subsequent analysis of the results. Initially, this research provides a tracking algorithm assessment system, including an assessment methodology and the collection of test videos, which helped us to determine that the suggested technique outperforms the KCF tracking method. Additionally, the implementation of an evaluation system allows for an objective comparison of the proposed algorithm with other prominent tracking methods. We found that the suggested method outperforms others in terms of its accuracy and resilience.
长期以来，实现快速可靠的图像目标跟踪对于图像引导技术的发展至关重要且具有挑战性。本研究通过提供基于核相关跟踪与检测方法的图像目标来研究实时目标跟踪，以应对复杂环境中实时目标跟踪的挑战。在跟踪过程中，核相关跟踪算法能够有效平衡跟踪性能和运行速度。然而，目标跟踪过程也面临诸如模型漂移、无法处理目标尺度变换以及目标长度等挑战。为了提出解决方案，本工作围绕以下要点展开：本研究的第一部分致力于核相关滤波器（KCF）的研究，包括模型训练、目标识别以及基于循环矩阵的密集采样策略。这项工作开发了一种尺度金字塔搜索方法来解决KCF无法预测目标尺度的缺点。跟踪器分两个阶段进行扩展：第一阶段输出目标的二维坐标位置，第二阶段创建尺度金字塔以确定最优目标尺度。实验表明，该方法能够解决目标尺寸变化问题。第二部分从两个方面对KCF进行了改进，以满足长期目标跟踪任务的需求。本文引入了初始目标模型，有效抑制了模型漂移。其次，实现了一个目标检测模块，如果跟踪模块失败，算法将重定向到目标检测模块。目标检测模块利用两个检测器，一个方差分类器和一个KCF。最后，本工作包括目标跟踪实验的试验以及对结果的后续分析。最初，本研究提供了一个跟踪算法评估系统，包括评估方法和测试视频的收集，这帮助我们确定所提出的技术优于KCF跟踪方法。此外，评估系统的实现允许将所提出的算法与其他著名跟踪方法进行客观比较。我们发现，所提出的方法在准确性和弹性方面优于其他方法。
