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

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.

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References

  1. 1.

    Ahonen T (2010) TomiAhonen Phone Book 2010. TomiAhonen Consulting

  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 1205–1210

  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 537–549

  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 381–384

  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 618–629

  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

    Article  Google 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 25:64–71

    Google 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 SIGCOMM

  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 80–89

  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 41–45

  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 252–253

  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):3–22

    Article  Google 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 287–288

  18. 18.

    Qualcomm: Vuforia sdk 1.5. Available at http://www.qualcomm.com/solutions/augmented-reality (2012)

  19. 19.

    Qualcomm: http://www.qualcomm.com/solutions/augmented-reality

  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 221–230

  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 2937–2944

  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 125–134

  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

    Article  Google 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 127–135

  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

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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.

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Correspondence to Juan M. Orduña.

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Fernández, V., Orduña, J.M. & Morillo, P. Comparative performance evaluation of CAR systems based on mobile phones and feature tracking. J Supercomput 70, 552–563 (2014). https://doi.org/10.1007/s11227-013-1082-9

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Keywords

  • Collaborative Augmented Reality
  • Natural feature tracking
  • Mobile phones