Tracking Registration Algorithm for Augmented Reality Based on Template Tracking
Tracking registration is a key issue in augmented reality applications, particularly where there are no artificial identifier placed manually. In this paper, an efficient markerless tracking registration algorithm which combines the detector and the tracker is presented for the augmented reality system. We capture the target images in real scenes as template images, use the random ferns classi- fier for target detection and solve the problem of reinitialization after tracking registration failures due to changes in ambient lighting or occlusion of targets. Once the target has been successfully detected, the pyramid Lucas-Kanade (LK) optical flow tracker is used to track the detected target in real time to solve the problem of slow speed. The least median of squares (LMedS) method is used to adaptively calculate the homography matrix, and then the three-dimensional pose is estimated and the virtual object is rendered and registered. Experimental results demonstrate that the algorithm is more accurate, faster and more robust.
KeywordsTracking registration augmented reality markerless random ferns Lucas-Kanade (LK) optical flow
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This work was supported by National Natural Science Foundation of China (No. 61125101).
- J. P. Lima, R. Roberto, F. Simões, M. Almeida, L. Figueiredo, J. M. Teixeira, V. Teichrieb. Markerless tracking system for augmented reality in the automotive industry. Expert Systems with Applications, vol. 82, pp. 100–114, 2017. DOI: https://doi.org/10.1016/j.eswa.2017.03.060.CrossRefGoogle Scholar
- F. P. Vista IV, D. J. Lee, K. T. Chong. Remote activation and confidence factor setting of ARToolKit with data association for tracking multiple markers. International Journal of Control and Automation, vol. 6, no. 6, pp. 243–252, 2013. DOI: https://doi.org/10.14257/ijca.2013.6.6.23.CrossRefGoogle Scholar
- B. Kang, P. Ren. Natural texture-based tracking algorithm for augmented reality. Systems Engineering and Electronics, vol. 31, no. 10, pp. 2480–2484, 2009. DOI: https://doi.org/10.3321/j.issn:1001-506X.2009.10.0440. (in Chinese)Google Scholar
- D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. DOI: https://doi.org/10.1023/B:VISI.0000029664.99615.94.CrossRefGoogle Scholar
- V. Lepetit, J. Pilet, P. Fua. Point matching as a classification problem for fast and robust object pose estimation. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Washington DC, USA, pp. 244–250, 2004. DOI: https://doi.org/10.1109/CVPR.2004.1315170.Google Scholar
- Y. Zhao, J. J. Li, H. P. Li, D. Yang. Real-time tracking and registration algorithm of scenarios of augmented reality based on improved random fern. Journal of Northeastern University (Natural Science), vol. 37, no. 5, pp. 614–618, 2016. DOI: https://doi.org/10.3969/j.issn.1005-3026.2016.05.002. (in Chinese)Google Scholar
- C. H. Yang, S. D. Liu, Z. M. Wang, Y. F. Guo, H. Li. Real-time vehicle matching based on random ferns. Journal of Xiamen University (Natural Science), vol. 53, no. 2, pp. 206–211, 2014. DOI: https://doi.org/10.6043/j.issn.0438-0479.2014.02.011. (in Chinese)CrossRefGoogle Scholar
- Y. Xie, X. D. Yang, Z. Liu, S. N. Ren, K. Chen. Method for visual localization of oil and gas wellhead based on distance function of projected features. International Journal of Automation and Computing, vol. 14, no. 2, pp. 147–158, 2017. DOI: https://doi.org/10.1007/s11633-017-1063-1.CrossRefGoogle Scholar
- J. S. Xu, Y. J. Wang, X. Cheng, S. Li, S. Y. Chen. Adaptive method for homography matrix estimation. Computer Engineering and Applications, vol. 52, no. 5, pp. 160–164, 2016. DOI: https://doi.org/10.3778/j.issn.1002-8331.1409-0357. (in Chinese)Google Scholar