DOI QR코드

DOI QR Code

A Geocoding Method on Character Matching in Indoor Spaces

실내 공간에서의 문자매칭 기반 지오코딩 기법

  • Received : 2013.01.02
  • Accepted : 2013.02.25
  • Published : 2013.02.28

Abstract

Recently, the use of locational information is growing rapidly. GPS technology has been adopted generally for obtaining locational information in outdoor spaces. In the other hand, the researches on indoor positioning have been carried out applying WLAN, RFID or Bluetooth technology because of the multi-path interference of GPS signal caused by the physical obstacles such as walls or columns in buildings. However, such technologies for indoor positioning cost too much to build sensing infrastructure and compute-intensive processes are involved. Furthermore, the accuracy of location estimation is variable caused by interior structures in buildings. In this study, to make up for the limitations, descriptive data such as phone number, unique room numbers, or business names readily available in mixed-use buildings is used for extracting location information. Furthermore, during the process, a geocoding method using character matching is applied to this study enabling prompt location estimation and sublating the fluctuation of accuracy caused by interior structures. Based on the proposed method in this study, an architecture is designed, and three-dimensional viewer program is developed for the implementation of this study. Also, this research is quantitatively analyzed through match rate and processing time of proposed method.

최근 위치 정보의 이용이 여러 분야에 걸쳐 급격하게 증가하고 있다. 실외에서는 위치 정보의 획득을 위해 일반적으로 GPS 기술을 사용하였으나, 복잡한 실내 공간에서는 벽, 기둥과 같은 물리적인 장애물들로 인해 발생한 다중경로 간섭으로 무선 근거리통신망, RFID, 블루투스 등의 무선 네트워크 기술을 적용한 연구가 진행되었다. 그러나 이러한 위치 측정 기술들은 센싱 인프라스트럭쳐 구축비용이 많이 들며, 측위에 있어 계산 집약적이고, 실내 구조에 따른 정확도의 변화가 발생하는 한계가 존재한다. 본 연구에서는 이러한 한계점을 보완하고자 복합용도건물 내에서 쉽게 획득 및 식별이 가능한 상가, 컨벤션 센터 및 오피스의 전화번호, 방 번호, 상호명과 같은 서술 데이터를 이용하여 위치를 추출한다. 이 과정에서 문자 매칭을 활용하며, 위치 추정에 있어 신속한 계산과 실내 환경에 따른 정확도 변화를 배제하기 위해 지오코딩 방법을 적용한다. 본 연구에서 제안된 방법을 아키텍쳐로 설계하며, 구현을 위해 3차원 가시화 프로그램을 개발한다. 또한 제한된 기법에서의 매칭률, 프로세싱 시간을 통하여 정략적으로 평가한다.

Keywords

References

  1. Android-OCR, https://github.com/rmtheis/android-ocr.
  2. Arica, N; Yarman-Vural, F. T. 2001, An overview of Character recognition focused on off-line handwriting, IEEE Transactions on systems, man, and cybernetics-Part C:Applications and Reviews, 31(2):1-22.
  3. Bekkali, A; Sanson, H; Matsumoto, M. 2007, RFID indoor positioning based on probabilistic RFID map and kalman filtering, In Proceedings of the IEEE International Conference on Wireless and Mobile Computing, Networking and Communication, 21-27.
  4. Bureau of the Census, U.S. 1970, Census Use Study: The DIME Geocoding System, Washington, DC: U.S. Bureau of the Census.
  5. Drummond, W. J. 1995, Addressingmatching: GIS technology formapping human activity patterns, APA Journal (Spring), 240-251.
  6. Feldmann, S; Kyamakya, K; Zapater, A; Lue, Z. 2003, An Indoor Bluetooth-Based Positioning System: Concept, Implementation and Experimental Evaluation, In Proceedings of the International Conference on Wireless Networks, 109-113.
  7. Finkenzeller, K. 2000, RFID Handbok: Radio-Frequency Identification Fundamentals and Applications, John Wiley & Sons.
  8. Gu, Y; Lo, A; Niemegeers, I. 2009, A survey of indoor positioning systems for wireless personal networks, IEEE Commun Surveys & Tutorials, 11(1):13-32. https://doi.org/10.1109/SURV.2009.090103
  9. Kaemarungsi, K; Krishnamurthy, P. 2004, Modeling of Indoor Positioning Systems Based on Location Fingerprinting, In Proceedings of the IEEE INFOCOM, 2:1012-1022.
  10. Ladd, A. M; Bekris, K. E; Rudys, A; Marceau, G; Kavraki, L. E; and Dan, S. 2002, Robotics-based location sensing using wireless Ethernet, Eighth ACM International Conference on Mobile Computing & Networking(MOBICOM), Atlanta, Georgia, US, 227-238.
  11. Le Cun, Y. et al. 1990, Handwritten Zip code recognition with multilayer networks, Proceedings of ICPR'90, 35-40.
  12. Lee, J. 2009, GIS-based geocoding methods for area-based addresses and 3D addresses in urban areas, Environment and Planning B : Planning and Design, 36:86-106. https://doi.org/10.1068/b31169
  13. Lee, S; Lee, J. 2011, Navigable Space-Relation Model for Indoor Space Analysis, Journal of Korea Spatial Information Society, 19(5):75-86.
  14. Lee, S. H; Park, S. H; Lee, J. 2010, 3D Adjacency Spatial Query using 3D Topological Network Data Model, Journal of Korea Spatial Information Society, 18(5):93-105.
  15. Li, B; Salter, J; Dempster, A. G; Rizos, C. 2006, Indoor positioning techniques based on wireless LAN, In 1st IEEE Int. Conf. on Wireless Broadband & Ultra Wideband Communications, Sydney, AUS.
  16. Mori, S; Suen, C. Y; Yamamoto, K; Sannella, M. J. 1992, Historical Review of OCR Research and Development, In Proceedings of the IEEE, 80(7):1029-1058. https://doi.org/10.1109/5.156468
  17. Ni, L. M; Yunhao, L; Yiu Cho, L. 2003, LANDMARC: indoor location sensing using active RFID, Pervasive Computing and Communications.
  18. Ordnance Survey. 2004, OSMasterMap address layer user guide, http://ordnancesurvey.co.uk
  19. Plamondon, R; Srihari, S. 2000, On-line and off-line handwriting recognition: A comprehensive survey, IEEE Transactions on pattern analysis and machine intelligence, 22(1):63-84. https://doi.org/10.1109/34.824821
  20. Smith, R; Antonova, D; Lee, D. 2009, MOCR '09 Adapting the Tesseract open source OCR engine for multilingual OCR, In Proc. Int. Workshop Multilingual OCR, Barcelona.
  21. Srihari, S.N; Govindaraju, V. 1989, Analysis of textual images using the Hough transform, Machine Vision Applications, 2:141-153. https://doi.org/10.1007/BF01212455
  22. Trier, O.D; Jaing, A.K; Taxt, T. 1996, Feature extraction method for character recognition-A survey, Pattern Recognition, 29(4):641-662. https://doi.org/10.1016/0031-3203(95)00118-2
  23. Wang, H; Szabo, A; Bamberger, J; Brunn, D; Hanebeck, U. 2008, Performance Comparison of Nonlinear Filters for Indoor WLAN Positioning, Proc. Int'l Conf. Information Fusion.
  24. Youssef, M; Agrawala, A; Shanker, A. U. 2003, WLAN location determination via clustering and probability distributions, IEEE International Conference on Pervasive Computing & Communications (PerCom) 2003, Fort Worth, Texas, US, 143-150.

Cited by

  1. FT-Indoornavi: A Flexible Navigation Method Based on Topology Analysis and Room Internal Path Networks for Indoor Navigation vol.21, pp.2, 2013, https://doi.org/10.12672/ksis.2013.21.2.001
  2. 실내공간 표준안 IndoorGML의 개념 및 활용 vol.21, pp.3, 2013, https://doi.org/10.12672/ksis.2013.21.3.001
  3. Topological Analysis in Indoor Shopping Mall using Ontology vol.31, pp.6, 2013, https://doi.org/10.7848/ksgpc.2013.31.6-2.511
  4. Using Omnidirectional Images for Semi-Automatically Generating IndoorGML Data vol.36, pp.5, 2013, https://doi.org/10.7848/ksgpc.2018.36.5.319