지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2024.11
- 수록면
- 45 - 51 (7page)
이용수
초록· 키워드
오류제보하기
Objective: Recent tragic accidents at large-scale events, where crowds gather, have underscored the critical importance of crowd management and safety monitoring. Thus, This study proposes a real-time crowd density monitoring system using drones, aimed at managing crowd density and preventing safety accidents.
METHODS: This study systematically analyzes the causes and processes of crowd crush accidents through fieldwork, data analysis, and a survey to explore prevention measures. The main factors identified include crowd density, lack of access control, and lack of safety guidelines. The survey results show that a significant number of respondents have experienced the risk of crowd crush in a crowded situation firsthand. Based on these results, this study identifies the causes of these accidents and provides real-time warnings by analyzing crowd movements and predicting potential risks using computer vision technology for effective crowd control and safety management. By delivering intuitive and immediate hazard warnings via LED signals from drones, directional speakers, bone conduction ear-cuffs, and entrance wristbands equipped with vibration sensors, it can effectively prevent large crowd incidents.
Conclusion: This study introduces a system that uses drones and computer vision technology to monitor crowd density in real-time and prevent safety accidents. It is particularly significant for presenting a technological solution to prevent large-scale crowd incidents, such as the Itaewon crush and the Hillsborough disaster. By analyzing crowd density based on drone-collected data and providing real-time danger warnings, the study introduces a new paradigm in safety management aimed at preventing accidents before they occur. Future research should address legal regulations and privacy concerns, while improving system effectiveness through more diverse experiments and field tests.
Application: This study suggests potential applications for police and public safety agencies and is expected to play a crucial role in preventing safety accidents at large-scale events.
METHODS: This study systematically analyzes the causes and processes of crowd crush accidents through fieldwork, data analysis, and a survey to explore prevention measures. The main factors identified include crowd density, lack of access control, and lack of safety guidelines. The survey results show that a significant number of respondents have experienced the risk of crowd crush in a crowded situation firsthand. Based on these results, this study identifies the causes of these accidents and provides real-time warnings by analyzing crowd movements and predicting potential risks using computer vision technology for effective crowd control and safety management. By delivering intuitive and immediate hazard warnings via LED signals from drones, directional speakers, bone conduction ear-cuffs, and entrance wristbands equipped with vibration sensors, it can effectively prevent large crowd incidents.
Conclusion: This study introduces a system that uses drones and computer vision technology to monitor crowd density in real-time and prevent safety accidents. It is particularly significant for presenting a technological solution to prevent large-scale crowd incidents, such as the Itaewon crush and the Hillsborough disaster. By analyzing crowd density based on drone-collected data and providing real-time danger warnings, the study introduces a new paradigm in safety management aimed at preventing accidents before they occur. Future research should address legal regulations and privacy concerns, while improving system effectiveness through more diverse experiments and field tests.
Application: This study suggests potential applications for police and public safety agencies and is expected to play a crucial role in preventing safety accidents at large-scale events.
목차
ABSTRACT
1. Introduction
2. Method
3. Results
4. Conclusion
References
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