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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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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