  Movie-Map, an interactive first-person-view map that engages the user in a
simulated walking experience, comprises short 360{\deg} video segments
separated by traffic intersections that are seamlessly connected according to
the viewer's direction of travel. However, in wide urban-scale areas with
numerous intersecting roads, manual intersection segmentation requires
significant human effort. Therefore, automatic identification of intersections
from 360{\deg} videos is an important problem for scaling up Movie-Map. In this
paper, we propose a novel method that identifies an intersection from
individual frames in 360{\deg} videos. Instead of formulating the intersection
identification as a standard binary classification task with a 360{\deg} image
as input, we identify an intersection based on the number of the possible
directions of travel (PDoT) in perspective images projected in eight directions
from a single 360{\deg} image detected by the neural network for handling
various types of intersections. We constructed a large-scale 360{\deg} Image
Intersection Identification (iii360) dataset for training and evaluation where
360{\deg} videos were collected from various areas such as school campus,
downtown, suburb, and china town and demonstrate that our PDoT-based method
achieves 88\% accuracy, which is significantly better than that achieved by the
direct naive binary classification based method. The source codes and a partial
dataset will be shared in the community after the paper is published.
