Point Anywhere: Directed Object Estimation from Omnidirectional Images
Nanami Kotani Asako Kanezaki
One of the intuitive instruction methods in robot navigation is a pointing
gesture. In this study, we propose a method using an omnidirectional camera to
eliminate the user/object position constraint and the left/right constraint of
the pointing arm. Although the accuracy of skeleton and object detection is low
due to the high distortion of equirectangular images, the proposed method
enables highly accurate estimation by repeatedly extracting regions of interest
from the equirectangular image and projecting them onto perspective images.
Furthermore, we found that training the likelihood of the target object in
machine learning further improves the estimation accuracy.