2022/04/10 by Naoki Sugimoto, Sugimoto, Naoki, Satoshi Ikehata +3 · 1 citation
Computer Science · Engineering · #Automated Road and Building Extraction #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Multimedia (cs.MM) #Video Surveillance and Tracking Methods #cs.CV #cs.MM
paper · pdf · doi:10.48550/arxiv.2204.04634
Accepted for publication in BMVC
arxiv created 2022/04/10 · openalex publication_date 2022/04/10 · arxiv updated 2022/04/12 · openalex created_date 2022/04/20 · openalex updated_date 2026/07/28
Movie-Map, an interactive first-person-view map that engages the user in a simulated walking experience, comprises short 360° 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° 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° videos. Instead of formulating the intersection identification as a standard binary classification task with a 360° 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° image detected by the neural network for handling various types of intersections. We constructed a large-scale 360° Image Intersection Identification (iii360) dataset for training and evaluation where 360° 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.