The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지
DOI QR코드

DOI QR Code

Framework Implementation of Image-Based Indoor Localization System Using Parallel Distributed Computing

병렬 분산 처리를 이용한 영상 기반 실내 위치인식 시스템의 프레임워크 구현

  • Kwon, Beom (Yonsei University, Department of Electrical and Electronic Engineering) ;
  • Jeon, Donghyun (Yonsei University, Department of Electrical and Electronic Engineering) ;
  • Kim, Jongyoo (Yonsei University, Department of Electrical and Electronic Engineering) ;
  • Kim, Junghwan (Yonsei University, Department of Electrical and Electronic Engineering) ;
  • Kim, Doyoung (Yonsei University, Department of Electrical and Electronic Engineering) ;
  • Song, Hyewon (Yonsei University, Department of Electrical and Electronic Engineering) ;
  • Lee, Sanghoon (Yonsei University, Department of Electrical and Electronic Engineering)
  • Received : 2016.08.17
  • Accepted : 2016.10.17
  • Published : 2016.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose an image-based indoor localization system using parallel distributed computing. In order to reduce computation time for indoor localization, an scale invariant feature transform (SIFT) algorithm is performed in parallel by using Apache Spark. Toward this goal, we propose a novel image processing interface of Apache Spark. The experimental results show that the speed of the proposed system is about 3.6 times better than that of the conventional system.

본 논문에서는 인메모리(In-memory) 병렬 분산 처리 시스템 Apache Spark(이하 Spark)를 활용하여 사용자에게 실시간 측위 정보를 제공할 수 있는 영상 기반 실내 위치인식 시스템을 제안한다. 제안하는 시스템에서는 사용자에게 실시간 측위 정보를 제공하기 위해서, Spark를 이용한 영상 특징점 추출 알고리즘의 병렬 분산화를 통해 알고리즘 연산 시간을 단축시킨다. 하지만 기존의 Spark 플랫폼에서는 영상 처리를 위한 인터페이스가 존재하지 않아, 영상 처리와 관련된 연산을 수행하는 것이 불가능하였다. 이에 본 논문에서는 Spark 영상 입출력 인터페이스를 구현하여 측위 연산을 위한 영상 처리를 Spark에서 수행 가능하게 하였다. 또한 무손실 압축(lossless compression)기법을 이용하여 특징점 기술자(descriptor)를 압축된 형태로 데이터베이스에 저장하여, 대용량의 실내 지도 데이터를 효율적으로 저장 및 관리하는 방법을 소개한다. 측위 실험은 실제 실내 환경에서 수행하였으며, 싱글 코어(Single-core) 시스템과의 성능 비교를 통해 제안하는 시스템이 최대 약 3.6배 단축된 시간으로 사용자에게 측위 정보를 제공 할 수 있다는 것을 입증하였다.

Keywords

References

  1. S.-H. Jung, M.-W. Lee, K.-Y. Kim, and J.-S. Cha, "Position information acquisition method based on LED lights and smart device camera using 3-Axis moving distance measurement," J. KICS, vol. 40, no. 1, pp. 226-232, Jan. 2015. https://doi.org/10.7840/kics.2015.40.1.226
  2. M. K. Jung and D. M. Lee, "Performance analysis of the localization compensation algorithm for moving objects using the least-squares method," J. KICS, vol. 39, no. 1, pp. 9-16, Jan. 2014.
  3. S. Son, T. Kim, Y. Jeon, and Y. Baek, "Smart camera technology to support high speed video processing in vehicular network," J. KICS, vol. 40, no. 1, pp. 152-164, Jan. 2015. https://doi.org/10.7840/kics.2015.40.1.152
  4. A. Irschara, et al., "From structure-frommotion point clouds to fast location recognition," in Proc. IEEE Conf. CVPR, pp. 2599-2606, 2009.
  5. D. Robertson and R. Cipolla, "An imagebased system for urban navigation," in Proc. British Machine Vision Conf., pp. 819-828, 2004.
  6. W. Zhang and J. Kosecka, "Image based localization in urban environments," in Proc. 3rd Int. Symp. 3D Data Process., Visualization, and Transmission, pp. 33-40, 2006.
  7. B, Kwon, et al., "A target position decision algorithm based on analysis of path departure for an autonomous path keeping system," Wireless Pers. Commun., vol. 83, no. 3, pp. 1843-1865, 2015. https://doi.org/10.1007/s11277-015-2485-0
  8. M. Zaharia, et al., "Spark: cluster computing with working sets," in Proc. HotCloud, pp. 10-16, 2010.
  9. J. Dean and S. Ghemawat, "Mapreduce: Simplified data processing on large clusters," Commun. ACM, vol. 51, no. 1, pp. 107-113, 2004. https://doi.org/10.1145/1327452.1327492
  10. M. Zaharia, et al., "Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing," in Proc. 9th USENIX Conf. Netw. Syst. Design and Implementation, Apr. 2012.
  11. S. Yang and B. Wu, "Large scale video data analysis based on spark," in Proc. IEEE Int. Conf. Cloud Comput. and Big Data, pp. 209-212, 2015.
  12. C. Sweeney, et al., HIPI: A hadoop image processing interface for image-based mapreduce tasks, University of Virginia, 2011.
  13. P. Moritz, et al., "SparkNet: training deep networks in Spark," arXiv preprint arXiv: 1511.06051, 2015.
  14. Y. Jia, et al., "Caffe: convolutional architecture for fast feature embedding," in Proc. 22nd ACM Int. Conf. Multimedia, pp. 675-678, Orlando, Florida, USA, Nov. 2014.
  15. R. Hartley and A. Zisserman, Multiple view geometry in computer vision, Cambridge University Press, 2003.
  16. D. Crandall, et al., "Discrete-continuous optimization for large-scale structure from motion," in Proc. IEEE CVPR, pp. 3001-3008, 2011.
  17. J. M. Frahm, et al., "Building rome on a cloudless day," in Proc. Eur. Conf. Computer Vision, Springer Berlin Heidelberg, pp. 368-381, 2010.
  18. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  19. H. Liu, et al., "Robust and accurate mobile visual localization and its applications," ACM Trans. Multimedia Comput. Commun., and Appl., vol. 9, no. 1s, p. 51, 2013.
  20. T. Sattler, et al., "Fast image-based localization using direct 2d-to-3d matching," in Proc. IEEE ICCV, pp. 667-674, Nov. 2011.
  21. Y. Li, et al., "Location recognition using prioritized feature matching," in Proc. Eur. Conf. Computer Vision, pp. 791-804, Springer Berlin Heidelberg, 2010.
  22. H. Bay, et al., "Speeded-up robust features (SURF)," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, 2008. https://doi.org/10.1016/j.cviu.2007.09.014
  23. K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Trans. Pattern Anal. and Machine Intell., vol. 27, no. 10, pp. 1615-1630, 2005. https://doi.org/10.1109/TPAMI.2005.188