Advertisement

The Journal of Supercomputing

, Volume 70, Issue 2, pp 552–563 | Cite as

Comparative performance evaluation of CAR systems based on mobile phones and feature tracking

  • Víctor Fernández
  • Juan M. OrduñaEmail author
  • Pedro Morillo
Article

Abstract

Collaborative Augmented Reality (CAR) systems based on mobile phones have experienced a huge expansion last years, since the hardware features of most mobile phones provide excellent multimedia services and wireless network capabilities. In previous works, we improved the performance of large-scale CAR systems based on mobile phones that use fiducial marker tracking. However, CAR systems based on natural feature tracking have just emerged, changing the way in which Augmented Reality applications work. In this paper, we propose the performance evaluation of CAR systems based on feature tracking when using mobile phones, and their comparison with CAR systems based on fiducial marker tracking. The evaluation of the whole CAR system includes the rendering of the virtual environment with Unity3D. The purpose is to provide the reader with a reference about the performance that can be achieved with each kind of CAR system. The evaluation results of client devices show that they work faster with natural feature (commonly denoted as markerless) tracking than with fiducial marker tracking, regardless of the phone model and the operating system considered. The evaluation results of the whole CAR system show that natural feature tracking provides similar performance than fiducial marker tracking when the system reaches saturation. However, the use of natural feature tracking allows better performance for low workloads or when the system approaches saturation, since, it provides similar response times at the cost of increasing the percentage of CPU utilization in the server, instead of dropping messages. These results validate natural feature tracking as the best option for CAR systems based on mobile phones.

Keywords

Collaborative Augmented Reality Natural feature tracking  Mobile phones 

Notes

Acknowledgments

This work has been jointly supported by the Spanish MICINN and the European Commission FEDER funds under grants TIN2009-14475-C04-04 and TIN2011-15734-E.

References

  1. 1.
    Ahonen T (2010) TomiAhonen Phone Book 2010. TomiAhonen ConsultingGoogle Scholar
  2. 2.
    Bauset VF, Orduña JM, Morillo P (2012) On the characterization of car systems based on mobile computing. In: Proceedings of IEEE 14th International Conference on High Performance Computing and Communication (HPCC-ICESS), pp 1205–1210Google Scholar
  3. 3.
    Bauset VF, Orduña JM, Morillo P (2012) Performance characterization of mobile phones in augmented reality marker tracking. In: Proceedings of the 12th International Conference on Computational and Mathematical Methods in Science and Engineering, vol 2, CMMSE ’12La Manga, Spain, pp 537–549Google Scholar
  4. 4.
    Bauset VF, Orduña JM, Morillo P (2013) How large scale car systems based on mobile phones should be implemented. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, GRAPP 2013, pp 381–384Google Scholar
  5. 5.
    Bauset VF, Orduña JM, Morillo P (2013) On the characterization of markerless car systems based on mobile phones. In: Proceedings of the 13th International Conference on Computational and Mathematical Methods in Science and Engineering, vol 2, CMMSE ’13Almeria, Spain, pp 618–629Google Scholar
  6. 6.
    Fernández V, Orduña JM, Morillo P (2013) How mobile phones perform in collaborative augmented reality (car) applications? J Supercomput 65:1179–1191. doi: 10.1007/s11227-013-0925-8 CrossRefGoogle Scholar
  7. 7.
    Goldstone W (2011) Unity 3.x Game development essentials. Community experience distilled. Packt Publishing. http://books.google.es/books?id=RJ5fsGXbqXwC
  8. 8.
    Hall SP, Anderson E (2009) Operating systems for mobile computing. J Comput Small Coll 25:64–71Google Scholar
  9. 9.
    Henderson T, Bhatti S (2003) Networked games: a qos-sensitive application for qos-insensitive users? In: Proceedings of the ACM SIGCOMM 2003, pp 141–147. ACM Press / ACM SIGCOMMGoogle Scholar
  10. 10.
    Henrysson A, Billinghurst M, Ollila M (2005) Face to face collaborative ar on mobile phones. In: Mixed and Augmented Reality, 2005. Proceedings Fourth IEEE and ACM International Symposium on, pp 80–89Google Scholar
  11. 11.
    Henrysson A, Ollila M (2004) Umar: ubiquitous mobile augmented reality. In: Proceedings of the 3rd international conference on Mobile and ubiquitous multimedia., MUM ’04ACM, New York, pp 41–45Google Scholar
  12. 12.
    Kato DH (2011) Artoolkit. Available at http://www.hitl.washington.edu/artoolkit/
  13. 13.
    Lee SE, Zhang Y, Fang Z, Srinivasan S, Iyer R, Newell D (2009) Accelerating mobile augmented reality on a handheld platform. In: Computer Design, 2009. ICCD 2009. IEEE International Conference on, pp 419–426. doi: 10.1109/ICCD.2009.5413123
  14. 14.
    Mahring M, Lessig C, Bimber O (2004) Video see-through ar on consumer cell-phones. In: ISMAR’04, pp 252–253Google Scholar
  15. 15.
    Nyatla (2011) Nyartoolkit:artoolkit class library for java/c#/android. Available at http://nyatla.jp/nyartoolkit/
  16. 16.
    Papagiannakis G, Singh G, Magnenat-Thalmann N (2008) A survey of mobile and wireless technologies for augmented reality systems. Comput Animat Virtual Worlds 19(1):3–22CrossRefGoogle Scholar
  17. 17.
    Piekarski W, Thomas BH (2002) Tinmith-hand: Unified user interface technology for mobile outdoor augmented reality and indoor virtual reality. In: Proceedings of virtual reality, pp 287–288Google Scholar
  18. 18.
    Qualcomm: Vuforia sdk 1.5. Available at http://www.qualcomm.com/solutions/augmented-reality (2012)
  19. 19.
  20. 20.
    de Sá M, Churchill E (2012) Mobile augmented reality: exploring design and prototyping techniques. In: Proceedings of the 14th international conference on human-computer interaction with mobile devices and services, MobileHCI ’12, ACM, New York, pp 221–230Google Scholar
  21. 21.
    Srinivasan S, Fang Z, Iyer R, Zhang S, Espig M, Newell D, Cermak D, Wu Y, Kozintsev I, Haussecker H (2009) Performance characterization and optimization of mobile augmented reality on handheld platforms. In: Workload characterization. IISWC 2009. IEEE International Symposium on, pp 128–137. doi: 10.1109/IISWC.2009.5306788
  22. 22.
    String ar sdk 1.3.1. Available at http://www.poweredbystring.com (2011)
  23. 23.
    Ta DN, Chen WC, Gelfand N, Pulli K (2009) Surftrac: Efficient tracking and continuous object recognition using local feature descriptors. IEEE Conference on Computer Vision and Pattern Recognition, pp 2937–2944Google Scholar
  24. 24.
    Wagner D, Reitmayr G, Mulloni A, Drummond T, Schmalstieg D (2008) Pose tracking from natural features on mobile phones. In: Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality, ISMAR ’08IEEE Computer Society, Washington, DC, pp 125–134Google Scholar
  25. 25.
    Wagner D, Reitmayr G, Mulloni A, Drummond T (2010) Real-time detection and tracking for augmented reality on mobile phones. IEEE Trans Vis Comput Graph 16(3):355–368. doi: 10.1109/TVCG.2009.99 CrossRefGoogle Scholar
  26. 26.
    Wagner D, Schmalstieg D (2003) First steps towards handheld augmented reality. In: Proceedings of the 7th IEEE International Symposium on Wearable Computers, ISWC ’03IEEE Computer Society, Washington, DC, pp 127–135Google Scholar
  27. 27.
    Wang S, Mao Z, Zeng C, Gong H, Li S, Chen B (2010) A new method of virtual reality based on unity3d. In: The 18th International Conference on Geoinformatics: GIScience in change, geoinformatics 2010, Peking University, Beijing, pp 1–5. IEEE (2010). doi: 10.1109/GEOINFORMATICS.2010.5567608

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Víctor Fernández
    • 1
  • Juan M. Orduña
    • 1
    Email author
  • Pedro Morillo
    • 1
  1. 1.Departamento de InformáticaUniversidad de ValenciaValenciaSpain

Personalised recommendations