Skip to main content
SpringerLink
Log in

Towards Efficient Mobile Augmented Reality in Indoor Environments

  • Conference paper
  • First Online:
Book cover Artificial Intelligence and Mobile Services – AIMS 2018 (AIMS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10970))

Included in the following conference series:

  • 1044 Accesses

Abstract

Augmented reality allows users to visualize annotations, videos, and images overlaid on physical objects through the use of a camera. However, the high computational processing cost of matching an image seen through a camera with that in an enormous database of images makes it daunting to use the concept of augmented reality on a smartphone. As matching an image with another takes time, some researchers leverage Global Positioning System (GPS) for localizing outdoor objects. Tagging images with GPS location reduces the number of images that are required to find a match which improves the overall efficiency. Unfortunately, this approach is not suitable for indoor environment as GPS does not work in indoor environments. To address this problem, we propose a system for mobile augmented reality (MAR) in indoor environments. By leveraging the already available Wi-Fi infrastructure, we estimate the location of the users inside a building to narrow down the search space. Furthermore, we utilize the smartphone motion sensors such as accelerometers and magnetometers to detect the phone’s direction towards an object, and also to capture the inclination degree of the smartphone to further reduce the search domain for an object. We deployed the system in a building at Florida State University. We tested our proposal and found that using the system we decreased the matching time significantly. Due to refining the search domain of the annotated image database, MAR uses the object recognition algorithm more efficiently and decreases the matching time from 2.8 s to just 17 ms with a total of 200 annotated images.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Takacs, G., Chandrasekhar, V., Gelfand, N., Xiong, Y., Chen, W.C., Bismpigiannis, T., Grzeszczuk, R., Pulli, K., Girod, B.: Outdoors augmented reality on mobile phone using loxel-based visual feature organization. In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval, pp. 427–434. ACM (2008)

    Google Scholar 

  2. Debnath, H., Borcea, C.: TagPix: automatic real-time landscape photo tagging for smartphones. In: 2013 International Conference on MOBILe Wireless MiddleWARE, Operating Systems and Applications (Mobilware), pp. 176–184. IEEE (2013)

    Google Scholar 

  3. Qin, C., Bao, X., Roy Choudhury, R., Nelakuditi, S.: TagSense: a smartphone-based approach to automatic image tagging. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, pp. 1–14. ACM (2011)

    Google Scholar 

  4. Jain, P., Manweiler, J., Roy Choudhury, R.: Overlay: practical mobile augmented reality. In: Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, pp. 331–344. ACM (2015)

    Google Scholar 

  5. Azuma, R.T.: A survey of augmented reality. Presence Teleoperators Virtual Environ. 6(4), 355–385 (1997)

    Article  Google Scholar 

  6. Piekarski, W., Thomas, B.: ARQuake: the outdoor augmented reality gaming system. Commun. ACM 45(1), 36–38 (2002)

    Article  Google Scholar 

  7. Yilmaz, R.M., Kucuk, S., Goktas, Y.: Are augmented reality picture books magic or real for preschool children aged five to six? Br. J. Educ. Technol. 48(3), 824–841 (2017)

    Article  Google Scholar 

  8. Yohan, S.J., Julier, S., Baillot, Y., Lanzagorta, M., Brown, D., Rosenblum, L.: BARS: battlefield augmented reality system. In: NATO Symposium on Information Processing Techniques for Military Systems, pp. 9–11

    Google Scholar 

  9. Arth, C., Schmalstieg, D.: Challenges of Large-Scale Augmented Reality on Smartphones, pp. 1–4. Graz University of Technology, Graz (2011)

    Google Scholar 

  10. Gotow, J.B., Zienkiewicz, K., White, J., Schmidt, D.C.: Addressing challenges with augmented reality applications on smartphones. In: Cai, Y., Magedanz, T., Li, M., Xia, J., Giannelli, C. (eds.) MOBILWARE 2010. LNICST, vol. 48, pp. 129–143. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17758-3_10

    Chapter  Google Scholar 

  11. Ma, R., Guo, Q., Hu, C., Xue, J.: An improved wifi indoor positioning algorithm by weighted fusion. Sensors 15(9), 21824–21843 (2015)

    Article  Google Scholar 

  12. Quan, M., Navarro, E., Peuker, B.: Wi-Fi localization using RSSI fingerprinting (2010)

    Google Scholar 

  13. Husen, M.N., Lee, S.: Indoor human localization with orientation using WiFi fingerprinting. In: Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, p. 109. ACM (2014)

    Google Scholar 

  14. Stook, J.: Planning an indoor navigation service for a smartphone with Wi-Fi fingerprinting localization. Master’s thesis (2012)

    Google Scholar 

  15. Roy, N., Wang, H., Roy Choudhury, R.: I am a smartphone and i can tell my user’s walking direction. In: Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, pp. 329–342. ACM (2014)

    Google Scholar 

  16. Shu, Y., Shin, K.G., He, T., Chen, J.: Last-mile navigation using smartphones. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pp. 512–524. ACM (2015)

    Google Scholar 

  17. Yang, J., Chen, Y.: Indoor localization using improved RSS-based lateration methods. In: Global Telecommunications Conference, GLOBECOM 2009, pp. 1–6. IEEE (2009)

    Google Scholar 

  18. Yang, J., Chen, Y., Martin, R.P., Trappe, W., Gruteser, M.: On the performance of wireless indoor localization using received signal strength. In: Handbook of Position Location: Theory, Practice, and Advances, pp. 395–424 (2011)

    Chapter  Google Scholar 

  19. Bahl, P., Padmanabhan, V.N.: Radar: an in-building RF-based user location and tracking system. In: Proceedings of Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2000, vol. 2, pp. 775–784. IEEE (2000)

    Google Scholar 

  20. Pedley, M.: Tilt sensing using a three-axis accelerometer. Freescale semiconductor application note, pp. 1–22 (2013)

    Google Scholar 

  21. Akherfi, K., Gerndt, M., Harroud, H.: Mobile cloud computing for computation offloading: issues and challenges. Appl. Comput. Inform. 14, 1–16 (2016)

    Article  Google Scholar 

  22. Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)

    Google Scholar 

  23. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32

    Chapter  Google Scholar 

  24. Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary Robust invariant scalable keypoints. In: 2011 International Conference on Computer Vision, pp. 2548–2555

    Google Scholar 

  25. Lv, Q., Josephson, W., Wang, Z., Charikar, M., Li, K.: Efficient indexing for high-dimensional similarity search. In: 33rd International Conference on Very Large Data Bases, pp. 950–961 (2007)

    Google Scholar 

  26. Petrosino, A.: Progress in Image Analysis and Processing, ICIAP 2013, Naples, Italy, 9–13 September 2013, Proceedings, vol. 8156. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41184-7

    Google Scholar 

  27. Li, F.-F., Andreetto, M., Ranzato, M.A., Perona, P.: Caltech 101

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Florida State University, Tallahassee, USA

    Mohammad Alahmadi & Jie Yang

Authors
  1. Mohammad Alahmadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Alahmadi .

Editor information

Editors and Affiliations

  1. University of Stuttgart, Stuttgart, Germany

    Marco Aiello

  2. Tsinghua University, Beijing, China

    Yujiu Yang

  3. Peking University, Beijing, China

    Yuexian Zou

  4. Kingdee International Software Group Co., Ltd., Shenzhen, China

    Liang-Jie Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alahmadi, M., Yang, J. (2018). Towards Efficient Mobile Augmented Reality in Indoor Environments. In: Aiello, M., Yang, Y., Zou, Y., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2018. AIMS 2018. Lecture Notes in Computer Science(), vol 10970. Springer, Cham. https://doi.org/10.1007/978-3-319-94361-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-94361-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94360-2

  • Online ISBN: 978-3-319-94361-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation