Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Escape Route Prediction and Tracking System using Artificial Intelligence

인공지능을 활용한 도주경로 예측 및 추적 시스템

  • Yang, Bum-Suk (Department of Convergence Engineering, Hoseo Graduate School of Venture) ;
  • Park, Dea-Woo (Department of Convergence Engineering, Hoseo Graduate School of Venture)
  • Received : 2022.07.31
  • Accepted : 2022.08.11
  • Published : 2022.08.31
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Abstract

In Seoul, about 75,000 CCTVs are installed in 25 district offices. Each ward office has built a control center for CCTV control and is performing 24-hour CCTV video control for the safety of citizens. Seoul Metropolitan Government is building a smart city integrated platform that is safe for citizens by providing CCTV images of the ward office to enable rapid response to emergency/emergency situations by signing an MOU with related organizations. In this paper, when an incident occurs at the Seoul Metropolitan Government Office, the escape route is predicted by discriminating people and vehicles using the AI DNN-based Template Matching technology, MLP algorithm and CNN-based YOLO SPP DNN model for CCTV images. In addition, it is designed to automatically disseminate image information and situation information to adjacent ward offices when vehicles and people escape from the competent ward office. The escape route prediction and tracking system using artificial intelligence can expand the smart city integrated platform nationwide.

서울특별시는 25개 구청에 7만5천여대의 CCTV가 설치되어 있다. 각 구청은 CCTV관제를 위한 관제센터를 구축하고 시민의 안전을 위해 24시간 CCTV영상관제를 수행하고 있다. 서울특별시는 유관기관과 MOU를 체결하여 긴급/응급 상황에 신속한 대응이 가능하도록 구청의 CCTV영상을 제공하여 시민이 안전한 스마트시티통합플랫폼을 구축하고 있다. 본 논문에서는, 서울특별시 관할구청에서 사건 발생 시, CCTV영상에 대해 인공지능 DNN 기반의 Template Matching 기술, MLP 알고리즘과 CNN 기반으로 YOLO SPP DNN모델을 사용하여 사람과 차량을 판별하여 도주경로를 예측한다. 또한, 관할구청을 이탈하여, 차량 및 사람이 도주 시, 인접 구청에 영상정보와 상황정보를 자동전파 하도록 설계한다. 인공지능을 활용한 도주경로 예측 및 추적 시스템은 스마트시티 통합플랫폼을 전국으로 확장시킬 수 있다.

Keywords

References

  1. T. W. Kim, H. H. Kim, and P. G. Kim, "Local Learning Support Archive System for Video Utilization in Integrated Control Center," in Proceedings of the Korean Society of Information Sciences, Pyeongchang, Korea, pp. 1212-1214, 2019.
  2. Y. H. Kim, "Osan City Empirical Case Study of Deep Learning-based Smart City Video Control Solution," Journal of the Korean Telecommunications Society, vol. 37, no. 5, pp. 42-48, Apr. 2020.
  3. Ministry of Land, Infrastructure and Transport Smart City Korea [Internet]. Avaiable: https://smartcity.go.kr/%ed94%84%eb%a1%9c%ec%a0%9d%ed%8a%b8/%ec%8a%a4%eba7%88%ed%8a%b8%eb%8f%84%ec%8b%9c-%ed%86%b5%ed%95%a9%ed%94%8c%eb%9e%ab%ed%8f%bc/.
  4. J. S. Kim, "A Study on the Promotion of Cooperation between CCTV Integrated Control Center and Related Organizations," The Police Science Journal, vol. 11, no. 4, pp. 295-318, Nov. 2016. https://doi.org/10.16961/POLIPS.2016.11.4.295
  5. Seoul open data center [Internet]. Avaiable: https://data.seoul.go.kr/.
  6. H. J. Lim and W. Kang, "The Recognition of the Crime Prevention Effects of the Integrated CCTV Control System: Focusing on Monitoring Personnel," Journal of the Korean Septid Society, vol. 12, no. 1, pp. 171-196, Apr. 2021.
  7. W. C. Choi and J. Y. Na, "Development of CCTV Cooperation Tracking System for Real-Time Crime Monitoring," Journal of the Korea Academia-Industrial cooperation Society, vol. 20, no. 12, pp. 546-554, Dec. 2019. https://doi.org/10.5762/KAIS.2019.20.12.546
  8. G. S. Lee, Y. J. Kim, and D. K. Go, "Development of vehicle number recognition program based on deep learning algorithm," Journal of the Korean Cadastre Information Association, vol. 22, no. 2, pp. 124-135, Apr. 2020. https://doi.org/10.46416/JKCIA.2020.08.22.2.124
  9. J. H. Kim and J. H. Lim, "License Plate Detection and Recognition Algorithm using Deep Learning," Institute of Korean Electrical and Electronics Engineers (IKEEE), vol. 23, no. 2, pp. 642-651, Jun. 2019.