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논문검색

Session 01 : 스마트시티

CNN 이미지 처리를 통한 교통사고 원인 분석

원문정보

Traffic Accident cause analysis from CNN image processing

박병화, 김형태, 윤용태

피인용수 : 0(자료제공 : 네이버학술정보)

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

저자정보

  • 박병화 Byung Hwa Park. 한양대
  • 김형태 Hyung Tae Kim. 서울대
  • 윤용태 Yong Tae Yoon. 서울대

참고문헌

자료제공 : 네이버학술정보

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