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Fast Markerless Tracking for Augmented Reality in Planar Environment

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3D Research

Abstract

Markerless tracking for augmented reality should not only be accurate but also fast enough to provide a seamless synchronization between real and virtual beings. Current reported methods showed that a vision-based tracking is accurate but requires high computational power. This paper proposes a real-time hybrid-based method for tracking unknown environments in markerless augmented reality. The proposed method provides collaboration of vision-based approach with accelerometers and gyroscopes sensors as camera pose predictor. To align the augmentation relative to camera motion, the tracking method is done by substituting feature-based camera estimation with combination of inertial sensors with complementary filter to provide more dynamic response. The proposed method managed to track unknown environment with faster processing time compared to available feature-based approaches. Moreover, the proposed method can sustain its estimation in a situation where feature-based tracking loses its track. The collaboration of sensor tracking managed to perform the task for about 22.97 FPS, up to five times faster than feature-based tracking method used as comparison. Therefore, the proposed method can be used to track unknown environments without depending on amount of features on scene, while requiring lower computational cost.

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References

  1. Zhou, F., Duh, H. B. L. & Billinghurst, M. (2008). Trends in augmented reality tracking, interaction and display: A review of ten years of Ismar. In Proceedings of the 2008 7th international symposium on mixed and augmented reality (ISMAR 2008): ACM & IEEE (pp. 193–202).

  2. Saba, T., & Altameem, A. (2013). Analysis of vision based systems to detect real time goal events in soccer videos. Applied Artificial Intelligence, 27(7), 656–667.

    Article  Google Scholar 

  3. Saba, T., & Rehman, A. (2012). Machine learning and script recognition (pp. 29–34). Saarbrücken: Lambert Academic Publisher.

    Google Scholar 

  4. Lee, T. and Hollerer, T. (2007). Handy Ar: Markerless inspection of augmented reality objects using fingertip tracking. In IEEE proceedings of the 2007 IEEE 11th IEEE international symposium on wearable computers (pp. 1–8).

  5. Soleimanizadeh, S., Mohamad, D., Saba, T., & Rehman, A. (2015). Recognition of partially occluded objects based on the three different color spaces (RGB, YCbCr, HSV). 3D Research,. doi:10.1007/s13319-015-0052-9.

    Google Scholar 

  6. Haron, H., Rehman, A., Adi, D. I. S., Lim, S. P., & Saba, T. (2012). Parameterization method on B-spline curve. Mathematical Problems in Engineering,. doi:10.1155/2012/640472.

    MathSciNet  Google Scholar 

  7. Klein, G., & Murray, D. (2007). Parallel tracking and mapping for small Ar workspaces. In Proceedings of the 2007 IEEE 6th international symposium on mixed and augmented reality (ISMAR 2007): IEEE & ACM (pp. 225–234). Orlando.

  8. Neubert, J., Pretlove, J., & Drummond, T. (2007). Semi-autonomous generation of appearance-based edge models from image sequences. In ISMAR ‘07 Proceedings of the 2007 6th IEEE and ACM international symposium on mixed and augmented reality (pp. 1–9). Washington DC.

  9. Uematsu, Y., & Saito, H. (2009). Multiple planes based registration using 3d projective space for augmented reality. Image and Vision Computing, 27, 1484–1496.

    Article  Google Scholar 

  10. Mooser, J., You, S., Neumann, U., & Wang, Q. (2009). Applying robust structure from motion to markerless augmented reality. In Proceedings of the 2009 IEEE 2009 workshop on applications of computer vision (WACV) (pp. 1–8).

  11. Siltanen, S. (2012). Theory and applications of marker-based augmented reality. Finland: VTT.

    Google Scholar 

  12. Rosten, E., & Drummond, T. (2005). Fusing points and lines for high performance tracking. In Proceedings of the 2005 IEEE international conference on computer vision (pp. 1508–1511).

  13. Charnley, D., & Blisett, R. (1988). Surface reconstruction from outdoor image sequences. In Proceedings of the 1988 Fourth Alvey Vision Club.

  14. Rehman, A., & Saba, T. (2014). Neural network for document image preprocessing. Artificial Intelligence Review, 42(2), 253–273. doi:10.1007/s10462-012-9337-z.

    Article  Google Scholar 

  15. Mundher, M., Muhamad, D., Rehman, A., Saba, T., & Kausar, F. (2014). Digital watermarking for images security using discrete slantlet transform. Applied Mathematics and Information Sciences, 8(6), 2823–2830. doi:10.12785/amis/080618.

  16. Lindeberg, T. (2011). Edge detection. Accessed November 22, 2012, from http://www.encyclopediaofmath.org/index.php?title=Edge_detection&oldid=17883.

  17. Marr, D., & Hildreth, E. (1980). Theory of Edge Detection. Proceedings of the Royal Society of London, 207, 187–217.

    Article  Google Scholar 

  18. Die Deutschen Versicherer (2012). Summary of Ship Movement. Accessed October 17, 2012, from http://www.containerhandbuch.de/chb_e/stra/index.html?/chb_e/stra/stra_02_03_03.html.

  19. Weisstein, E. W. (2012). Euler angles. Accessed October 17, 2012, from http://mathworld.wolfram.com/EulerAngles.html.

  20. Kato, H., & Billinghurst, M. (1999). Marker tracking and hmd calibration for a video-based augmented reality conferencing system. In Proceedings of the 1999 IEEE computer society and 2nd IEEE and ACM international workshop on augmented reality (IWAR’99) (pp. 85–94). Washington DC.

  21. Mizell, D. (2003). Using gravity to estimate accelerometer orientation. In Proceedings of the 2003 seventh IEEE international symposium of wearable computers (pp. 252–253).

  22. Muhsin, Z. F., Rehman, A., Altameem, A., Saba, T., & Uddin, M. (2014). Improved quadtree image segmentation approach to region information. The Imaging Science Journal, 62(1), 56–62.

    Article  Google Scholar 

  23. Luinge, H. J., & Veltink, P. H. (2005). Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Medical & Biological Engineering & Computing, 43(2), 273–282.

    Article  Google Scholar 

  24. Weisstein, E. W. (2013). Plane. MathWorld—A Wolfram Web Resource. Retrieved June 22, 2013, from http://mathworld.wolfram.com/Plane.html.

  25. Azuma, R. (1995). Predictive tracking for augmented reality. PhD Thesis, UNC-Chapel Hill.

  26. Neamah, K., Mohamad, D., Saba, T., & Rehman, A. (2014). Discriminative features mining for offline handwritten signature verification. 3D Research,. doi:10.1007/s13319-013-0002-3.

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Computing and Information Technology Rabigh, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia

    Ahmad Hoirul Basori & Hamza Ali S. AbuJabal

  2. Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Fadhil Noer Afif

  3. College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia

    Abdulaziz S. Almazyad

  4. College of Computer and Information Systems, Al-Yamamah University, Riyadh, Kingdom of Saudi Arabia

    Abdulaziz S. Almazyad & Amjad Rehman

  5. Faculty of Information Sciences & Engineering, Management and Science University Shah Alam, Selangor, Malaysia

    Mohammed Hazim Alkawaz

  6. Faculty of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq

    Mohammed Hazim Alkawaz

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  1. Ahmad Hoirul Basori
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  2. Fadhil Noer Afif
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  3. Abdulaziz S. Almazyad
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  4. Hamza Ali S. AbuJabal
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  5. Amjad Rehman
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  6. Mohammed Hazim Alkawaz
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Corresponding author

Correspondence to Amjad Rehman.

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Basori, A.H., Afif, F.N., Almazyad, A.S. et al. Fast Markerless Tracking for Augmented Reality in Planar Environment. 3D Res 6, 41 (2015). https://doi.org/10.1007/s13319-015-0072-5

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  • DOI: https://doi.org/10.1007/s13319-015-0072-5

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