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Markerless Tracking for Augmented Reality Using Different Classifiers

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Book cover The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 407))

Abstract

Augmented reality (AR) is the combination of a real scene viewed by the user and a virtual scene generated by the computer that augments the scene with additional information. The user of an AR application should feel that the augmented object is a part of the real world. One of the factors that greatly affect this condition is the tracking technique used. In this paper, an augmented reality application is adopted with markerless tracking as a classification task. ORB algorithm is used for feature detection and the FREAK algorithm is used for feature description. The classifiers used for the tracking task are KNN, Random Forest, Extremely Randomized Trees, SVM and Bayes classifier. The performance of each classifier used is evaluated in terms of speed and efficiency. It has been observed that KNN outperforms other classifiers including Random Forest with different number of trees.

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References

  1. Martínez, H., Skournetou, D., Hyppölä, J., Laukkanen, S., Heikkilä, A.: Drivers and bottlenecks in the adoption of augmented reality applications. J. Multimedia Theory Appl. 2(1), 27–44 (2014)

    Google Scholar 

  2. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  3. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 36(1), 3–42 (2006)

    Article  MATH  Google Scholar 

  4. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)

    Article  MATH  Google Scholar 

  5. Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  6. Rish, I.: An empirical study of the naive bayes classifier. In: International Joint Conference on Artificial Intelligence vol. 3(22), pp. 41–46 (2001)

    Google Scholar 

  7. Barandiaran, I., Paloc, C., Grana, M.: Real-time optical markerless tracking for augmented reality applications. J. Real-Time Image Proc. 5(2), 129–138 (2010)

    Article  Google Scholar 

  8. Lee, A., Lee, J., Lee, S., Choi, J.: Markerless augmented reality system based on planar object tracking. In: Proceeding of Frontiers of Computer Vision, pp. 1–4 (2011)

    Google Scholar 

  9. Lee, T., Hollerer, T.: Multithreaded hybrid feature tracking for markerless augmented reality. IEEE Trans. Visual Comput. Graphics 15(3), 355–368 (2009)

    Article  Google Scholar 

  10. Wagner, D., Schmalstieg, D., Bischof, H.: Multiple target detection and tracking with guaranteed framerates on mobile phones. In: Proceedings of the 8th IEEE International Symposium on Mixed and Augmented Reality (ISMAR’09), pp. 57–64 (2009)

    Google Scholar 

  11. Kim, K., Lepetit, V., Woo, W.: Scalable real-time planar targets tracking for digilog books. Visual Comput. 26(6–8), 1145–1154 (2010)

    Article  Google Scholar 

  12. Park, Y., Lepetit, V., Woo, W.: Multiple 3D object tracking for augmented reality. In: Proceedings of the 7th IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR’08), pp. 117–120 (2008)

    Google Scholar 

  13. Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)

    Article  Google Scholar 

  14. Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: an accurate O(n) solution to the pnp problem. Int. J. Comput. Vision 81(2), 155–166 (2009)

    Article  Google Scholar 

  15. Davison, A., Mayol, W., Murray, D.: Real-time localisation and mapping with wearable active vision. In: Proceeding 2nd IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR’03), (2003)

    Google Scholar 

  16. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2564–2571 (2011)

    Google Scholar 

  17. Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: fast retina keypoint. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ‘12), pp. 510–517 (2012)

    Google Scholar 

  18. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  19. Bradski, G.: The opencv library. Doctor Dobbs J. 25(11), 120–126 (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Menofia University, Menofia, Egypt

    Faten A. Khalifa, Noura A. Semary, Hatem M. El-Sayed & Mohiy M. Hadhoud

Authors
  1. Faten A. Khalifa
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  2. Noura A. Semary
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  3. Hatem M. El-Sayed
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  4. Mohiy M. Hadhoud
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Corresponding author

Correspondence to Faten A. Khalifa .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  2. Department of Information Technology, Cairo University Faculty of Computers & Information, Giza, Egypt

    Aboul Ella Hassanien

  3. College of Management and Technology, Arab Academy for Science, Technology, and Maritime Transport, Giza, Egypt

    Nashwa El-Bendary

  4. Bengal College of Engineering and Technology, Durgapur, West Bengal, India

    Nilanjan Dey

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Khalifa, F.A., Semary, N.A., El-Sayed, H.M., Hadhoud, M.M. (2016). Markerless Tracking for Augmented Reality Using Different Classifiers. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-26690-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26688-6

  • Online ISBN: 978-3-319-26690-9

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