Earticle

초록

영어

Streets are one of the major interface of human activities in urban areas. Urban environment and urban dwellers interact each others in the urban streets. Analysing the urban environment is important for us to understand the potential impact of the urban built environment on urban dwellers. The publicly accessible Google Street View (GSV), which captures the streetscape appearances of cities around the world, presents a very good tool for urban studies at a fine level. In this study, we will use KoRoad’s ‘Frequent traffic accident location information’ for detailed spot location data and GSV for corresponding source of street-level image data for environmental component analysis.Convolutional neural network, also known as convnets or CNN, is a well-known method in computer vision applications to recognize objects from a picture or video. The purpose of the convolution is to extract the features of the object on the images locally so that the network can learn specific patterns within the picture and will be able to recognize it everywhere in the picture. In this paper, we describe our plan of using CNN classification method for analyzing the cause of traffic accidents with the data from GSV and KoRoad GPS information.

목차

Abstract
I. Introduction
II. Data Preperation
III. Convolutional Neural Network Classification Plan
IV. Conclusion
References

키워드

저자

• 박병화 [ Byung Hwa Park | 한양대 ]
• 김형태 [ Hyung Tae Kim | 서울대 ]
• 윤용태 [ Yong Tae Yoon | 서울대 ] 교신저자

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국정보통신설비학회 학술대회

• 간기

  반년간

• 수록기간

  2002~2025

• 십진분류

  KDC 567 DDC 621