Yadav Srishti, Payandeh Shahram
Networked Robotics and Sensing Laboratory, School of Engineering Science, Simon Fraser University, Burnaby, Canada.
Multimed Syst. 2023;29(1):401-420. doi: 10.1007/s00530-022-00996-6. Epub 2022 Oct 6.
Unlike deep learning which requires large training datasets, correlation filter-based trackers like Kernelized Correlation Filter (KCF) use implicit properties of tracked images (circulant structure) for training in real time. Despite their popularity in tracking applications, there exists significant drawbacks of the tracker in cases like occlusions and out-of-view scenarios. This paper attempts to address some of these drawbacks with a novel RGB-D Kernel Correlation tracker in target re-detection. Our target re-detection framework not only re-detects the target in challenging scenarios but also intelligently adapts to avoid any boundary issues. Our results are experimentally evaluated using (a) standard dataset and (b) real time using the Microsoft Kinect V2 sensor. We believe this work will set the basis for improvement in the effectiveness of kernel-based correlation filter trackers and will further the development of a more robust tracker.
与需要大量训练数据集的深度学习不同，基于相关滤波器的跟踪器，如核相关滤波器（KCF），利用被跟踪图像的隐式属性（循环结构）进行实时训练。尽管它们在跟踪应用中很受欢迎，但在遮挡和视野外场景等情况下，跟踪器存在显著缺点。本文试图通过一种新颖的RGB-D核相关跟踪器来解决目标重新检测中的一些缺点。我们的目标重新检测框架不仅能在具有挑战性的场景中重新检测目标，还能智能地进行调整以避免任何边界问题。我们的结果通过（a）标准数据集进行实验评估，并（b）使用微软Kinect V2传感器进行实时评估。我们相信这项工作将为改进基于核的相关滤波器跟踪器的有效性奠定基础，并推动更强大跟踪器的发展。
