Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
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A Study on the Walkability Scores in Jeonju City Using Multiple Regression Models

다중 회귀 모델을 이용한 전주시 보행 환경 점수 예측에 관한 연구

  • 이기춘 (군산대학교 공과대학 컴퓨터정보공학과) ;
  • 남광우 (군산대학교 공과대학 컴퓨터정보공학과) ;
  • 이창우 (군산대학교 공과대학 컴퓨터정보공학과)
  • Received : 2022.08.02
  • Accepted : 2022.08.24
  • Published : 2022.08.30
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Abstract

Attempts to interpret human perspectives using computer vision have been developed in various fields. In this paper, we propose a method for evaluating the walking environment through semantic segmentation results of images from road images. First, the Kakao Map API was used to collect road images, and four-way images were collected from about 50,000 points in JeonJu. 20% of the collected images build datasets through crowdsourcing-based paired comparisons, and train various regression models using paired comparison data. In order to derive the walkability score of the image data, the ranking score is calculated using the Trueskill algorithm, which is a ranking algorithm, and the walkability and analysis using various regression models are performed using the constructed data. Through this study, it is shown that the walkability of Jeonju can be evaluated and scores can be derived through the correlation between pixel distribution classification information rather than human vision.

컴퓨터 비전을 활용하여 인간의 시각을 해석하려는 시도가 다양한 분야에서 발전되어 왔다. 본 논문에서는 도로영상으로부터 영상의 의미론적 분할 결과를 통해 보행 환경을 평가하는 방법을 제안한다. 먼저 도로영상을 수집하기 위해 카카오 지도 API를 활용하였으며 전주시지역의 약 5만 점에서 4방향 영상을 수집한다. 수집된 영상의 20%는 크라우드 소싱기반 쌍체 비교를 통해 데이터 셋을 구축하고, 쌍체 비교 데이터를 이용하여 다양한 회귀 모델을 훈련한다. 영상 데이터의 보행성 점수를 도출하기 위해 순위 알고리즘인 Trueskill 알고리즘을 활용하여 랭킹 점수를 계산하고, 구축된 데이터를 활용하여 다양한 회귀모델을 사용한 보행성 평가 및 분석 작업을 수행한다. 본 연구를 통해 사람의 시각이 아닌 픽셀 분포 분류 정보 간의 상관관계를 통해 컴퓨터 시스템만으로 전주시의 보행 환경을 평가하고 점수를 도출해 낼 수 있다는 것을 보여준다.

Keywords

Acknowledgement

이 논문은 한국국토정보공사 공간정보연구원 산학협력 R&D 사업의 지원(No.2021-07380001)과 국토교통부/국토교통과학기술진흥원의 지원(과제번호 RS-2022-00143336) 으로 연구되었음.

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