  Person re-identification (PRID) from side-mounted rectilinear-lens cameras is
a well-studied problem. On the other hand, PRID from overhead fisheye cameras
is new and largely unstudied, primarily due to the lack of suitable image
datasets. To fill this void, we introduce the "Fisheye Re-IDentification
Dataset with Annotations" (FRIDA), with 240k+ bounding-box annotations of
people, captured by 3 time-synchronized, ceiling-mounted fisheye cameras in a
large indoor space. Due to a field-of-view overlap, PRID in this case differs
from a typical PRID problem, which we discuss in depth. We also evaluate the
performance of 10 state-of-the-art PRID algorithms on FRIDA. We show that for 6
CNN-based algorithms, training on FRIDA boosts the performance by up to 11.64%
points in mAP compared to training on a common rectilinear-camera PRID dataset.
