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P‐202: Late‐News Poster: Dynamic Obstacle Detection to Improve BVI Pedestrian's Navigation Decision using CNNs
Author(s) -
Yun Borin,
Kwon Jangwoo
Publication year - 2018
Publication title -
sid symposium digest of technical papers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.351
H-Index - 44
eISSN - 2168-0159
pISSN - 0097-966X
DOI - 10.1002/sdtp.12462
Subject(s) - obstacle , pedestrian , computer science , artificial intelligence , pedestrian detection , computer vision , geography , engineering , transport engineering , archaeology
Two CNNs models are proposed to detect dynamic obstacles in urban setting so as to improve BVI pedestrian's navigation decision. First, we re‐implement the inspired model, yolov2, on KITTI datasets. Regarding evaluation, the two models perform better than yolov2 from 6.26% to 10.99% on car detection at different difficulty levels.