Abstract
Real-time 3D pose estimation from monocular image sequences is a challenging research topic. Although current methods are able to recover 3D pose, they are severely challenged by the computational cost. To address this problem, we propose a tracking and 3D pose estimation method supported by three main pillars: a pyramidal structure, an aspect graph and the checkpoints. Once initialized the systems performs a top-down tracking. At a high level it detects the position of the object and segments its time-space trajectory. This stage increases the stability and the robustness for the tracking process. Our main objective is the 3D pose estimation, the pose is estimated only in relevant events of the segmented trajectory, which reduces the computational effort required. In order to obtain the 3D pose estimation in the complete trajectory, an interpolation method, based on the aspect graph describing the structure of the object’s surface, can be used to roughly estimate the poses between two relevant events. This early version of the method has been developed to work with a specific type of polyhedron with strong edges, texture and differentiated faces, a die.
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Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.J.: Surf: Speeded up robust features. Computer Vision and Image Understanding (CVIU)Â 110(3) (2008)
Brun, L., Kropatsch, W.G.: Construction of Combinatorial Pyramids. In: Hancock, E.R., Vento, M. (eds.) GbRPR 2003. LNCS, vol. 2726, pp. 1–12. Springer, Heidelberg (2003)
Gauglitz, S., Höllerer, T., Turk, M.: Evaluation of interest point detectors and feature descriptors for visual tracking. International Journal of Computer Vision, 1–26 (2011)
Horn, B.K.P.: Closed-form solution of absolute orientation using unit. J. Optical Society of America 4(4), 629–642 (1987)
Klein, G., Murray, D.: Parallel tracking and mapping for small ar workspaces. In: Proc. of ISMAR (2007)
Kropatsch, W.G., Bischof, H., Englert, R.: Hierarchies. In: Digital Image Analysis: Selected Techniques and Applications, ch. III Robust and Adaptive Image Understanding (2001)
Lepetit, V., Fua, P.: Monocular model-based 3d tracking of rigid objects: A survey. Foundations and Trends in Computer Graphics and Vision 1(1) (2005)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Computer Vision and Image Understanding 20 (2004)
Marfil, R., Molina-Tanco, L., RodrÃguez, S.F.: Real-time object tracking using bounded irregular pyramids. Pattern Recognition Letters (2007)
Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (2010)
Ramachandran, G., Kropatsch, W.: Using aspect graphs for view synthesis. In: Proceedings of Computer Vision Winter Workshop, CVWW (2012)
Ravela, S., Draper, B., Lim, J., Weiss, R.: Adaptive tracking and model registration across distinct aspects. In: International Conference on Intelligent Robots and Systems (1995)
Sinha, S.N., Frahm, J.M., Pollefeys, M., Genc, Y.: Gpu-based video feature tracking and matching. In: EDGE, Workshop on Edge Computing Using New Commodity Architectures (2006)
Torres, F., Kropatsch, W.G., Artner, N.M.: Predict pose and position of rigid objects in video sequences. In: Proceedings of International Conference on Systems, Signals and Image Processing, IWSSIP (2012)
Wagner, D., Reitmayr, G., Mulloni, A., Drummond, T., Schmalstieg, D.: Pose tracking from natural features on mobile phones. In: International Symposium on Mixed and Augmented Reality, Cambridge, UK (2008)
Wagner, D., Schmalstieg, D., Bischof, H.: Multiple target detection and tracking with guaranteed framerates on mobile phones. In: Proceedings of Int. Symposium on Mixed and Augmented Reality (2009)
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Torres, F., Kropatsch, W.G. (2012). Top-Down Tracking and Estimating 3D Pose of a Die. In: Gimel’farb, G., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_54
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DOI: https://doi.org/10.1007/978-3-642-34166-3_54
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