  Target tracking in hyperspectral videos is a new research topic. In this
paper, a novel method based on convolutional network and Kernelized Correlation
Filter (KCF) framework is presented for tracking objects of interest in
hyperspectral videos. We extract a set of normalized three-dimensional cubes
from the target region as fixed convolution filters which contain spectral
information surrounding a target. The feature maps generated by convolutional
operations are combined to form a three-dimensional representation of an
object, thereby providing effective encoding of local spectral-spatial
information. We show that a simple two-layer convolutional networks is
sufficient to learn robust representations without the need of offline training
with a large dataset. In the tracking step, KCF is adopted to distinguish
targets from neighboring environment. Experimental results demonstrate that the
proposed method performs well on sample hyperspectral videos, and outperforms
several state-of-the-art methods tested on grayscale and color videos in the
same scene.
