  In this paper, we present our deep learning-based human detection system that
uses optical (RGB) and long-wave infrared (LWIR) cameras to detect, track,
localize, and re-identify humans from UAVs flying at high altitude. In each
spectrum, a customized RetinaNet network with ResNet backbone provides human
detections which are subsequently fused to minimize the overall false detection
rate. We show that by optimizing the bounding box anchors and augmenting the
image resolution the number of missed detections from high altitudes can be
decreased by over 20 percent. Our proposed network is compared to different
RetinaNet and YOLO variants, and to a classical optical-infrared human
detection framework that uses hand-crafted features. Furthermore, along with
the publication of this paper, we release a collection of annotated
optical-infrared datasets recorded with different UAVs during search-and-rescue
field tests and the source code of the implemented annotation tool.
