DATaR: depth augmented target redetection using kernelized correlation filter

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 exist 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.