How mobile phones perform in collaborative augmented reality (CAR) applications


This paper presents the experimental analysis of mobile phones for Augmented Reality marker tracking, a core task that any CAR application must include. The results show that the most time consuming stage is the marker detection stage, followed by the image acquisition stage. Moreover, the rendering stage is decoupled on some devices, depending on the operative system used. This decoupling process allows avoiding low refresh rates, facilitating the collaborative work. However, the use of multicore devices does not significantly improve the performance provided by CAR applications. Finally, the results show that unless a poor network bandwidth makes the network to become the system bottleneck, the performance of CAR applications based on mobile phones will be limited by the detection stage. These results can be used as the basis for an efficient design of CAR systems and applications based on mobile phones.

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  1. 1.

    Ahonen T (2010) TomiAhonen phone book 2010. TomiAhonen Consulting

    Google Scholar 

  2. 2.

    Apple: iOS 4 (2011). Available at

  3. 3.

    Bauset VF, Orduña JM, Morillo P (2011) Performance characterization on mobile phones for collaborative augmented reality (car) applications. In: Proc of IEEE/ACM 15th international symposium on distributed simulation and real time applications (DS-RT ’11), pp 52–53

    Google Scholar 

  4. 4.

    Billinghurst M, Kato H (1999) Real world teleconferencing. In: Proc of the conference on human factors in computing systems (CHI 99)

    Google Scholar 

  5. 5.

    Billinghurst M, Poupyrev I, Kato H, May R (2000) Mixing realities in shared space: an augmented reality interface for collaborative computing. In: IEEE international conference on multimedia and expo (ICME 2000), vol 3, pp 1641–1644. doi:10.1109/ICME.2000.871085

    Google Scholar 

  6. 6.

    Fiala M (2005) Artag: a fiducial marker system using digital techniques. In: Proc of IEEE conf on computer vision and pattern recognition (CVPR), vol 2, pp 590–596

    Google Scholar 

  7. 7.

    Google: Android (2011). Available at

  8. 8.

    Hall S, Anderson E (2009) Operating systems for mobile computing. J Comput Small Coll 25:64–71

    Google Scholar 

  9. 9.

    Hallerer T, Feiner S, Terauchi T, Rashid G (1999) Exploring mars: developing indoor and outdoor user interfaces to a mobile augmented reality system. Comput Graph 23:779–785

    Article  Google Scholar 

  10. 10.

    Henrysson A, Billinghurst M, Ollila M (2005) Face to face collaborative ar on mobile phones. In: Proc of 4th international symposium on mixed and augmented reality, pp 80–89

    Google 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 ’04), pp 41–45

    Google Scholar 

  12. 12.

    Lee SE, Zhang Y, Fang Z, Srinivasan S, Iyer R, Newell D (2009) Accelerating mobile augmented reality on a handheld platform. In: IEEE international conference on computer design (ICCD 2009), pp 419–426. doi:10.1109/ICCD.2009.5413123

    Google Scholar 

  13. 13.

    Loulier B (2011) Augmented reality on iphone using artoolkitplus. Available at

  14. 14.

    Loulier B (2011) Virtual reality on iphone. Available at

  15. 15.

    Mahring M, Lessig C, Bimber O (2004) Video see-through ar on consumer cell-phones. In: ISMAR ’04, pp 252–253

    Google Scholar 

  16. 16.

    Nyartoolkit: Nyartoolkit code (2011). Available at

  17. 17.

    Nyatla: Nyartoolkit: artoolkit class library for java/c#/android (2011). Available at

  18. 18.

    Piekarski W, Thomas BH (2002) Tinmith-hand: unified user interface technology for mobile outdoor augmented reality and indoor virtual reality

  19. 19.

    Schmalstieg D, Wagner D (2007) Experiences with handheld augmented reality. In: 6th IEEE and ACM international symposium on mixed and augmented reality (ISMAR 2007), pp 3–18. doi:10.1109/ISMAR.2007.4538819

    Google Scholar 

  20. 20.

    Schmeil A, Broll W (2007) An anthropomorphic AR-based personal information manager and guide. In: Proceedings of the 4th international conference on universal access in human–computer interaction: ambient interaction (UAHCI ’07). Springer, Berlin, pp 699–708

    Google Scholar 

  21. 21.

    Thomas MHMBB (2007) Emerging technologies of augmented reality: interfaces and design. IGI Global. doi:10.4018/978-1-59904-066-0

  22. 22.

    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 ’08). IEEE Comput Soc, Los Alamitos, pp 125–134

    Google Scholar 

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This work has been jointly supported by the Spanish MICINN and the European Commission FEDER funds, under grants TIN2009-14475-C04-04, and TIN2010-12011-E.

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Correspondence to Juan M. Orduña.

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Fernández, V., Orduña, J.M. & Morillo, P. How mobile phones perform in collaborative augmented reality (CAR) applications. J Supercomput 65, 1179–1191 (2013).

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  • Collaborative augmented reality
  • Marker tracking
  • Mobile phones