Authors:
Zhangchi Lu
;
Mertcan Cokbas
;
Prakash Ishwar
and
Janusz Konrad
Affiliation:
Department of Electrical and Computer Engineering, Boston University, 8 Saint Mary’s Street, Boston, MA 02215, U.S.A.
Keyword(s):
Distance Estimation, Fisheye, MLP, Deep Learning.
Abstract:
Unobtrusive monitoring of distances between people indoors is a useful tool in the fight against pandemics. A natural resource to accomplish this are surveillance cameras. Unlike previous distance estimation methods, we use a single, overhead, fisheye camera with wide area coverage and propose two approaches. One method leverages a geometric model of the fisheye lens, whereas the other method uses a neural network to predict the 3D-world distance from people-locations in a fisheye image. For evaluation, we collected a first-of-its-kind dataset, Distance Estimation between People from Overhead Fisheye cameras (DEPOF), using a single fisheye camera, that comprises a wide range of distances between people (1–58ft) and is publicly available. The algorithms achieve 20-inch average distance error and 95% accuracy in detecting social-distance violations.