  Unlike deep learning which requires large training datasets, correlation
filter-based trackers like Kernelized Correlation Filter (KCF) uses implicit
properties of tracked images (circulant matrices) for training in real-time.
Despite their practical application in tracking, a need for a better
understanding of the fundamentals associated with KCF in terms of
theoretically, mathematically, and experimentally exists. This thesis first
details the workings prototype of the tracker and investigates its
effectiveness in real-time applications and supporting visualizations. We
further address some of the drawbacks of the tracker in cases of occlusions,
scale changes, object rotation, out-of-view and model drift with our novel
RGB-D Kernel Correlation tracker. We also study the use of particle filters to
improve trackers' accuracy. 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 a better understanding of the
effectiveness of kernel-based correlation filter trackers and to further define
some of its possible advantages in tracking.
