Hy-Tracker: A Novel Framework for Enhancing Efficiency and Accuracy of
  Object Tracking in Hyperspectral Videos
Mohammad Aminul Islam Wangzhi Xing Jun Zhou Yongsheng Gao Kuldip K. Paliwal
Hyperspectral object tracking has recently emerged as a topic of great
interest in the remote sensing community. The hyperspectral image, with its
many bands, provides a rich source of material information of an object that
can be effectively used for object tracking. While most hyperspectral trackers
are based on detection-based techniques, no one has yet attempted to employ
YOLO for detecting and tracking the object. This is due to the presence of
multiple spectral bands, the scarcity of annotated hyperspectral videos, and
YOLO's performance limitation in managing occlusions, and distinguishing object
in cluttered backgrounds. Therefore, in this paper, we propose a novel
framework called Hy-Tracker, which aims to bridge the gap between hyperspectral
data and state-of-the-art object detection methods to leverage the strengths of
YOLOv7 for object tracking in hyperspectral videos. Hy-Tracker not only
introduces YOLOv7 but also innovatively incorporates a refined tracking module
on top of YOLOv7. The tracker refines the initial detections produced by
YOLOv7, leading to improved object-tracking performance. Furthermore, we
incorporate Kalman-Filter into the tracker, which addresses the challenges
posed by scale variation and occlusion. The experimental results on
hyperspectral benchmark datasets demonstrate the effectiveness of Hy-Tracker in
accurately tracking objects across frames.