Occlusion Aware Kernel Correlation Filter Tracker using RGB-D
This work addresses occlusion and other tracking issues for real-time applications, but it appears incremental as it builds on existing correlation filter methods.
The paper tackled the problem of improving object tracking under challenges like occlusions and scale changes by developing a novel RGB-D Kernel Correlation tracker, achieving experimental evaluation on standard datasets and real-time using a Kinect V2 sensor.
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.