DOI QR코드

DOI QR Code

Multiple Object Tracking and Identification System Using CCTV and RFID

감시 카메라와 RFID를 활용한 다수 객체 추적 및 식별 시스템

  • 김진아 (호서대학교 컴퓨터공학과) ;
  • 문남미 (호서대학교 컴퓨터소프트웨어전공)
  • Received : 2016.07.28
  • Accepted : 2016.11.23
  • Published : 2017.02.28

Abstract

Because of safety and security, Surveillance camera market is growing. Accordingly, Study on video recognition and tracking is also actively in progress, but There is a limit to identify object by obtaining the information of object identified and tracked. Especially, It is more difficult to identify multiple objects in open space like shopping mall, airport and others utilized surveillance camera. Therefore, This paper proposed adding object identification function by using RFID to existing video-based object recognition and tracking system. Also, We tried to complement each other to solve the problem of video and RFID based. Thus, through the interaction of system modules We propose a solution to the problems of failing video-based object recognize and tracking and the problems that could be cased by the recognition error of RFID. The system designed to identify the object by classifying the identification of object in four steps so that the data reliability of the identified object can be maintained. To judge the efficiency of this system, this demonstrated by implementing the simulation program.

안전과 보안상의 이유로 감시 카메라의 시장이 확대되고 있으며 이에 대해 영상 인식 및 추적에 관한 연구도 활발히 진행 중에 있으나 인식 및 추적되는 객체의 정보를 획득하여 객체를 식별하는 데는 한계가 있다. 특히, 감시카메라가 활용되는 쇼핑몰, 공항 등과 같은 개방된 공간에서는 다수의 객체들을 식별하기란 더욱 어렵다. 따라서 본 논문에서는 기존의 영상기반 객체 인식 및 추적 시스템에 RFID 기술을 더하여 객체 식별기능을 추가하고자 하였으며 영상 기반과 RFID의 문제 해결을 위해 상호 보완하고자 하였다. 그리하여 시스템의 모듈별 상호작용을 통해 영상기반 객체 인식 및 추적에 실패할 수 있는 문제와 RFID의 인식 오류로 발생할 수 있는 문제에 대한 해결 방안을 제시하였다. 객체의 식별 정도를 4단계로 분류하여 가장 최상의 단계로 객체가 식별이 되도록 시스템을 설계해 식별된 객체의 데이터 신뢰성을 유지할 수 있도록 하였다. 시스템의 효율성 판단을 위해 시뮬레이션 프로그램을 구현하여 이를 입증하였다.

Keywords

References

  1. The Ministry of Government Administration and Home Affairs, "CCTV Survey and Privacy Comprehensive Support System Status Data," 2015.
  2. H. S. Yoon, "The Trend of Technology and Market of Image Recognition Service," Communications of the Korean Institute of Information Scientists and Engineers, Vol. 31, No. 2, pp. 23-31, 2013.
  3. H. S. Ho, "IP Camera Market and Technology Trends in the Video Security Industry," Review of Kiisc, Vol. 20, No. 3, pp. 18-23, 2010.6
  4. G. N. Ko, Y. S. Lee, and N. M. Moon, "People Counting System by Facial Age Group," Journal of the Institute of Electronics and Information Engineers, Vol. 51, No. 2, pp. 69-75, 2014. https://doi.org/10.5573/ieie.2014.51.2.069
  5. J. H. Baek, J. Y. Min, S. Namkoong, and S. H. Yoon, "An In-Tunnel Traffic Accident Detection Algorithm using CCTV Image Processing," KIPS Transactions on Software and Data Engineering, Vol. 4, No. 2, pp. 83-90, 2015. https://doi.org/10.3745/KTSDE.2015.4.2.83
  6. S. N. Heo, H. S. Son, and B. I. Moon, "Multiple Moving Object Detection Using Different Algorithms," Journal of the Korean Institute of Communication Sciences, Vol. 40, No. 9, pp. 1828-1836, 2015. https://doi.org/10.7840/kics.2015.40.9.1828
  7. U. M. Prakash and V. G. Thamaraiselvi, "Detecting and tracking of multiple moving objects for intelligent video surveillance systems," in Current Trends in Engineering and Technology (ICCTET), 2014 2nd International Conference on. IEEE, pp. 253-257, 2014.
  8. H. So, K. H. Lee, D. K. Choi, and H. J. Lee, "Real-time Tracking of Object in Sports Videos via Particle Filter," in Proceedings of the Korean Information Science Society Conference, pp. 1557-1559. 2014.
  9. Y. H. Kwon and Y. G. Chae, "An Improved Object Recognition and Tracking Algorithm Based on Block Matching," Journal of the Korean Institute of Information Technology, Vol. 13, No. 4, pp. 61-68, 2015. https://doi.org/10.14801/jkiit.2015.13.4.61
  10. C. Garate, S. Zaidenberg, J. Badie, and F. Bremond, "Group tracking and behavior recognition in long video surveillance sequences," in Computer Vision Theory and Applications (VISAPP), 2014 International Conference on. IEEE, Vol. 2, pp. 366-402, 2014.
  11. Y. B. Shim and H. J. Park, "A Study on a Violence Recognition System with CCTV," Journal of the Digital Contents Society, Vol. 16, No. 1, pp. 25-32, 2015. https://doi.org/10.9728/dcs.2015.16.1.25
  12. J. W. Park and S. Y. Kwak, "Detection of Crowd Escape Behavior in Surveillance Video," Journal of the Korean Institute of Communication Sciences, Vol. 39, No. 8, pp. 731-737, 2014.
  13. S. Lee and J. S. Cho, "Tracking and Recognition of vehicle and pedestrian for intelligent multi-visual surveillance systems," Journal of the Korea Institute of Information and Communication Engineering, Vol. 19, No. 2, pp. 435-442. 2015. https://doi.org/10.6109/jkiice.2015.19.2.435
  14. C. S. Chung, "A Case Study on the Operation Enhancement of Integrated CCTV Control Center at Busan Metropolitan City," The Journal of Korean Associastion for Regional Information Society, Vol. 18, No. 3, pp. 123-154, 2015.
  15. M. H, Jung, "[Special Issue : RFID Technology Trends] RFID Standardization," The Proceedings of the Korea Electromagnetic Engineering Society, Vol. 15, No. 2, pp12-20, 2004.
  16. H. J. Choi and S. J. Moon, "Trends and Development Prospects of RFID Technology," in Proceedings of the Korea Intelligent Information System Society Conference, pp. 387-390, 2011.