Large-Scale Person Detection and Localization using Overhead Fisheye
  Cameras
Lu Yang Liulei Li Xueshi Xin Yifan Sun Qing Song Wenguan Wang
Location determination finds wide applications in daily life. Instead of
existing efforts devoted to localizing tourist photos captured by perspective
cameras, in this article, we focus on devising person positioning solutions
using overhead fisheye cameras. Such solutions are advantageous in large field
of view (FOV), low cost, anti-occlusion, and unaggressive work mode (without
the necessity of cameras carried by persons). However, related studies are
quite scarce, due to the paucity of data. To stimulate research in this
exciting area, we present LOAF, the first large-scale overhead fisheye dataset
for person detection and localization. LOAF is built with many essential
features, e.g., i) the data cover abundant diversities in scenes, human pose,
density, and location; ii) it contains currently the largest number of
annotated pedestrian, i.e., 457K bounding boxes with groundtruth location
information; iii) the body-boxes are labeled as radius-aligned so as to fully
address the positioning challenge. To approach localization, we build a fisheye
person detection network, which exploits the fisheye distortions by a
rotation-equivariant training strategy and predict radius-aligned human boxes
end-to-end. Then, the actual locations of the detected persons are calculated
by a numerical solution on the fisheye model and camera altitude data.
Extensive experiments on LOAF validate the superiority of our fisheye detector
w.r.t. previous methods, and show that our whole fisheye positioning solution
is able to locate all persons in FOV with an accuracy of 0.5 m, within 0.1 s.