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http://dx.doi.org/10.7840/kics.2016.41.11.1490

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

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)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.41, no.11, 2016 , pp. 1490-1501 More about this Journal
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.
Keywords
Image-based localization; Apache Spark; In-memory parallel distributed computing; Lossless compression; Lempel-Ziv;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 D. Crandall, et al., "Discrete-continuous optimization for large-scale structure from motion," in Proc. IEEE CVPR, pp. 3001-3008, 2011.
2 J. M. Frahm, et al., "Building rome on a cloudless day," in Proc. Eur. Conf. Computer Vision, Springer Berlin Heidelberg, pp. 368-381, 2010.
3 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.   DOI
4 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.
5 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.   DOI
6 A. Irschara, et al., "From structure-frommotion point clouds to fast location recognition," in Proc. IEEE Conf. CVPR, pp. 2599-2606, 2009.
7 D. Robertson and R. Cipolla, "An imagebased system for urban navigation," in Proc. British Machine Vision Conf., pp. 819-828, 2004.
8 T. Sattler, et al., "Fast image-based localization using direct 2d-to-3d matching," in Proc. IEEE ICCV, pp. 667-674, Nov. 2011.
9 D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.   DOI
10 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.
11 Y. Li, et al., "Location recognition using prioritized feature matching," in Proc. Eur. Conf. Computer Vision, pp. 791-804, Springer Berlin Heidelberg, 2010.
12 H. Bay, et al., "Speeded-up robust features (SURF)," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, 2008.   DOI
13 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.   DOI
14 J. Dean and S. Ghemawat, "Mapreduce: Simplified data processing on large clusters," Commun. ACM, vol. 51, no. 1, pp. 107-113, 2004.   DOI
15 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.
16 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.   DOI
17 M. Zaharia, et al., "Spark: cluster computing with working sets," in Proc. HotCloud, pp. 10-16, 2010.
18 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.
19 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.
20 C. Sweeney, et al., HIPI: A hadoop image processing interface for image-based mapreduce tasks, University of Virginia, 2011.
21 P. Moritz, et al., "SparkNet: training deep networks in Spark," arXiv preprint arXiv: 1511.06051, 2015.
22 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.
23 R. Hartley and A. Zisserman, Multiple view geometry in computer vision, Cambridge University Press, 2003.