• Title/Summary/Keyword: 공익제보

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Analysis of the difference between ideal and reality that whistle-blowing people experience: focus on phenomenological method (공익제보자들이 경험하는 이상과 현실의 차이: 현상학적 분석)

  • Hu, Sungho;Lee, Geunyoung;Jung, Taeyun
    • Korean Journal of Culture and Social Issue
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    • v.23 no.1
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    • pp.25-52
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    • 2017
  • The purpose of this study is to explore the motivation for, expectations of, consequences of, and attitudes toward whistle-blowing. that whistle-blowers experience in the whistle-blowing process, and clarify the difference between the ideal and reality. Fourteen official whistle-blowers were interviewed and qualitative data were analyzed based on phenomenological approach. As a result, motivational themes appeared such as professional spirit and personal values. They also tried to pursue public interest such as social changes as well as public support for them. In addition, the participants believed that the cultural characteristics of Korean society such as vertical collectivism are responsible for the gap between ideals and reality of whistle-blowing. In particular, They also suggested a few alternative for reducing the gap.

Object Detection Method for Developing a Path Change Violation Image Analysis System (진로변경 위반 영상 분석을 위한 객체 인식 방법)

  • Choi, Min-Seong;Choi, Bongjun;Moon, Mikyeong
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2022.07a
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    • pp.499-500
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    • 2022
  • 차량용 블랙박스의 대중화와 '스마트 국민 제보' 애플리케이션 도입에 따른 영향으로 교통법규 위반 공익신고 건수가 급증하면서 대응해야 할 담당 경찰 인력이 부족한 상황이다. 이러한 인력 부족 문제를 해결하기 위해서 인공지능(AI) 알고리즘을 활용하여 신고된 영상의 위법 여부를 자동으로 분석할 필요가 있다. 본 논문에서는 공익신고의 대부분을 차지하고 있는 진로변경 위반 영상 분석을 위한 객체 인식 방법에 대한 연구 내용을 기술한다. 이 연구에서는 딥러닝 알고리즘과 컴퓨터 비전 알고리즘을 통해 진로변경 위반 분석에 필요한 차량과 실선 객체를 인식하여 진로변경 위반 영상 분석에 활용할 수 있도록 한다.

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Analysis System for Public Interest Report Video of Traffic Law Violation based on Deep Learning Algorithms (딥러닝 알고리즘 기반 교통법규 위반 공익신고 영상 분석 시스템)

  • Min-Seong Choi;Mi-Kyeong Moon
    • The Journal of the Korea institute of electronic communication sciences
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    • v.18 no.1
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    • pp.63-70
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    • 2023
  • Due to the spread of high-definition black boxes and the introduction of mobile applications such as 'Smart Citizens Report' and 'Safety Report', the number of public interest reports for violations of Traffic Law has increased rapidly, resulting in shortage of police personnel to handle them. In this paper, we describe the development of a system that can automatically detect lane violations which account for the largest proportion of public interest reporting videos for violations of traffic laws, using deep learning algorithms. In this study, a method for recognizing a vehicle and a solid line object using a YOLO model and a Lanenet model, a method for tracking an object individually using a deep sort algorithm, and a method for detecting lane change violations by recognizing the overlapping range of a vehicle object's bounding box and a solid line object are described. Using this system, it is expected that the shortage of police personnel in charge will be resolved.