Abstract
We present a markerless tracking approach for augmented reality in poorly textured environments. The approach enables a robust and accurate camera pose estimation merely on basis of a coarse edge model. The edge model of the object to be tracked is enhanced and refined during the tracking process. New edges are added to the edge model and already existing ones are refined. A collection of reference poses with a set of corresponding edges, called keyposes, enables a selection of good edges to track depending on the current view and makes the tracking process robust and accurate. Keyposes are also used to reinitialize automatically after tracking failures, e.g. the object to be tracked is occluded. Therefore, the proposed method overcomes the limitations of traditionally used edge based tracking approaches in terms of reinitialization and edge model creation. Evaluation on synthetic and real image sequences demonstrates the significant improvement of the proposed method over a standard edge based tracking.
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Notes
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Real-time Attitude and Position Determination.
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The calculation of the tracking quality is based on the covariance matrix and the number of detected edges in the current frame.
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Acknowledgments
Part of this research was done for Fraunhofer IDM@NTU, which is funded by the National Research Foundation (NRF) and managed through the multi-agency Interactive & Digital Media Programme Office (IDMPO).
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Hebborn, A.K., Erdt, M., Müller, S. (2015). Robust Model Based Tracking Using Edge Mapping and Refinement. In: De Paolis, L., Mongelli, A. (eds) Augmented and Virtual Reality. AVR 2015. Lecture Notes in Computer Science(), vol 9254. Springer, Cham. https://doi.org/10.1007/978-3-319-22888-4_9
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DOI: https://doi.org/10.1007/978-3-319-22888-4_9
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