Towards Automatic 3D Pose Tracking through Polygon Mesh Approximation

  • Manlio Barajas
  • Jorge Esparza
  • J. L. Gordillo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7637)


A method for visual 3D pose tracking of objects whose shape can be approximated to a polygon mesh it’s presented. The proposed method takes advantage of the fact that polygon meshes may be composed of quadrilaterals, which can be tracked in 2D using standard plane tracking for which homography decomposition can be used to recover 3D pose information. Results show that it’s feasible to do 3D pose tracking of polygon meshes using only one monocular camera and 2D tracking. This is a first step for a full automatic 3D pose tracking system, since planes can be detected without any priori knowledge using automatic plane detection methods.


3D tracking 3D pose estimation approximated 3D models homography decomposition cuboid tracking polyhedral tracking polygon mesh tracking visual tracking automatic object tracking 


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  1. 1.
    Bouchafa, S., Zavidovique, B.: Obstacle Detection “for Free” in the C-Velocity Space. In: 14th International IEEE Conference on Transportation Systems (ITSC 2011), pp. 308–313. IEEE (2011)Google Scholar
  2. 2.
    Bouchafa, S., Zavidovique, B.: c-Velocity: A Flow-Cumulating Uncalibrated Approach for 3D Plane Detection. International Journal of Computer Vision 97, 148–166 (2011)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Brown, J.A., Capson, D.W.: A Framework for 3D Model-Based Visual Tracking Using a GPU-Accelerated Particle Filter. IEEE Transactions on Visualization and Computer Graphics 18(1), 68–80 (2011)CrossRefGoogle Scholar
  4. 4.
    Cobzas, D., Sturm, P.: 3D SSD Tracking with Estimated 3D Planes. In: 2nd Canadian Conference on Computer and Robot Vision (CRV 2005), pp. 129–134 (2005)Google Scholar
  5. 5.
    Košecká, J., Zhang, W.: Extraction, Matching, and Pose Recovery Based on Dominant Rectangular Structures. Computer Vision and Image Understanding 100(3), 274–293 (2005)CrossRefGoogle Scholar
  6. 6.
    Manz, M., Luettel, T.: Monocular Model-Based 3D Vehicle Tracking for Autonomous Vehicles in Unstructured Eenvironment. In: IEEE International Conference on Robotics and Automation (ICRA 2011), pp. 2465–2471. IEEE (2011)Google Scholar
  7. 7.
    Benhimane, S., Malis, E.: Homography-Based 2D Visual Tracking and Servoing. The International Journal of Robotics Research 26(1), 661–676 (2007)CrossRefGoogle Scholar
  8. 8.
    Micusik, B., Wildenauer, H.: Towards Detection of Orthogonal Planes in Monocular Images of Indoor Environments. In: IEEE International Conference on Robotics and Automation (ICRA 2008), pp. 999–1004. IEEE (2008)Google Scholar
  9. 9.
    Munozsalinas, R., Aguirre, E., Garciasilvente, M.: People Detection and Tracking Using Stereo Vision and Color. Image and Vision Computing 25(6), 995–1007 (2007)CrossRefGoogle Scholar
  10. 10.
    Panin, G., Knoll, A.: Mutual Information-Based 3D Object Tracking. International Journal of Computer Vision 78(1), 107–118 (2007)CrossRefGoogle Scholar
  11. 11.
    Prisacariu, V.A., Timofte, R., Zimmermann, K., Reid, I., Gool, L.V.: Integrating Object Detection with 3D Tracking Towards a Better Driver Assistance System. In: 20th International Conference on Pattern Recognition (ICPR 2010), pp. 3344–3347 (2010)Google Scholar
  12. 12.
    Shao, X., Zhao, H., Nakamura, K., Katabira, K., Shibasaki, R., Nakagawa, Y.: Detection and Tracking of Multiple Pedestrians by Using Laser Range Scanners. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), pp. 2174–2179. IEEE (2007)Google Scholar
  13. 13.
    Song, X., Zhao, H., Cui, J., Shao, X., Shibasaki, R., Zha, H.: Fusion of Laser and Vision for Multiple Targets Tracking Via On-Line Learning. In: IEEE International Conference on Robotics and Automation (ICRA 2010), pp. 406–411. IEEE (2010)Google Scholar
  14. 14.
    Tonko, M., Nagel, H.H.: Model-Based Stereo-Tracking of Non-Polyhedral Objects for Automatic Disassembly Experiments. International Journal of Computer Vision 37(1), 99–118 (2000)MATHCrossRefGoogle Scholar
  15. 15.
    Malis, E.: Improving Vision-Based Control Using Efficient Second-Order Minimization Techniques. In: IEEE International Conference on Robotics and Automation (ICRA 2004), vol. 2, pp. 1843–1848. IEEE (2004)Google Scholar
  16. 16.
    Baker, S., Matthews, I.: Lucas-Kanade 20 Years On – A Unifying Framework – Part 1. International Journal of Computer Vision 56, 221–255 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Manlio Barajas
    • 1
  • Jorge Esparza
    • 1
  • J. L. Gordillo
    • 1
  1. 1.Center for Intelligent SystemsTecnológico de MonterreyMonterreyMéxico

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