Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt.
Technical Research and Development Center, Cairo, Egypt.
PLoS One. 2022 Aug 25;17(8):e0273022. doi: 10.1371/journal.pone.0273022. eCollection 2022.
Visual object tracking is a critical problem in the field of computer vision. The visual object tracker methods can be divided into Correlation Filters (CF) and non-correlation filters trackers. The main advantage of CF-based trackers is that they have an accepted real-time tracking response. In this article, we will focus on CF-based trackers, due to their key role in online applications such as an Unmanned Aerial Vehicle (UAV), through two contributions. In the first contribution, we proposed a set of new video sequences to address two uncovered issues of the existing standard datasets. The first issue is to create two video sequence that is difficult to be tracked by a human being for the movement of the Amoeba under the microscope; these two proposed video sequences include a new feature that combined background clutter and occlusion features in a unique way; we called it hard-to-follow-by-human. The second issue is to increase the difficulty of the existing sequences by increasing the displacement of the tracked object. Then, we proposed a thorough, practical evaluation of eight CF-base trackers, with the top performance, on the existing sequence features such as out-of-view, background clutters, and fast motion. The evaluation utilized the well-known OTB-2013 dataset as well as the proposed video sequences. The overall assessment of the eight trackers on the standard evaluation metrics, e.g., precision and success rates, revealed that the Large Displacement Estimation of Similarity transformation (LDES) tracker is the best CF-based tracker among the trackers of comparison. On the contrary, with a deeper analysis, the results of the proposed video sequences show an average performance of the LDES tracker among the other trackers. The eight trackers failed to capture the moving objects in every frame of the proposed Amoeba movement video sequences while the same trackers managed to capture the object in almost every frame of the sequences of the standard dataset. These results outline the need to improve the CF-based object trackers to be able to process sequences with the proposed feature (i.e., hard-to-follow-by-human).
视觉目标跟踪是计算机视觉领域的一个关键问题。视觉目标跟踪方法可分为相关滤波器 (CF) 和非相关滤波器跟踪器。基于 CF 的跟踪器的主要优势在于它们具有可接受的实时跟踪响应。在本文中，我们将重点介绍基于 CF 的跟踪器，因为它们在无人机 (UAV) 等在线应用中起着关键作用，主要通过两个贡献来实现。在第一个贡献中，我们提出了一组新的视频序列，以解决现有标准数据集未涵盖的两个问题。第一个问题是创建两个难以被人类跟踪的视频序列，这些序列中的阿米巴虫在显微镜下运动；这两个新提出的视频序列包括一个新的特征，以独特的方式结合了背景杂波和遮挡特征；我们称之为难以被人类跟踪。第二个问题是通过增加跟踪对象的位移来增加现有序列的难度。然后，我们对八个基于 CF 的跟踪器进行了彻底、实际的评估，这些跟踪器在现有的序列特征（如视场外、背景杂波和快速运动）上表现最好。评估利用了著名的 OTB-2013 数据集以及新提出的视频序列。八个跟踪器在标准评估指标（如精度和成功率）上的整体评估表明，相似性变换的大位移估计 (LDES) 跟踪器是比较中最好的基于 CF 的跟踪器。相反，通过更深入的分析，新提出的视频序列的结果显示，在其他跟踪器中，LDES 跟踪器的平均性能较差。八个跟踪器无法在新提出的阿米巴虫运动视频序列的每一帧中捕捉到运动物体，而同一跟踪器能够在标准数据集的序列中的几乎每一帧中捕捉到物体。这些结果表明，需要改进基于 CF 的目标跟踪器，以便能够处理具有新提出的难以被人类跟踪特征的序列。
