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Segmentation-Free, Area-Based Articulated Object Tracking

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Advances in Visual Computing (ISVC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6938))

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Abstract

We propose a novel, model-based approach for articulated object detection and pose estimation that does not need any low-level feature extraction or foreground segmentation and thus eliminates this error-prone step. Our approach works directly on the input color image and is based on a new kind of divergence of the color distribution between an object hypothesis and its background. Consequently, we get a color distribution of the target object for free.

We further propose a coarse-to-fine and hierarchical algorithm for fast object localization and pose estimation. Our approach works significantly better than segmentation-based approaches in cases where the segmentation is noisy or fails, e.g. scenes with skin-colored backgrounds or bad illumination that distorts the skin color.

We also present results by applying our novel approach to markerless hand tracking.

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References

  1. Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and chamfer matching: Two new techniques for image matching. In: International Joint Conference on Artificial Intelligence (1977)

    Google Scholar 

  2. Borgefors, G.: Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE Transaction on Pattern Analysis and Machine Intelligence (1988)

    Google Scholar 

  3. Huttenlocher, D., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence (1993)

    Google Scholar 

  4. Athitsos, V., Sclaroff, S.: 3d hand pose estimation by finding appearance-based matches in a large database of training views. In: IEEE Workshop on Cues in Communication (2001)

    Google Scholar 

  5. Athitsos, V., Sclaroff, S.: An appearance-based framework for 3d hand shape classification and camera viewpoint estimation. In: IEEE Conference on Automatic Face and Gesture Recognition (2002)

    Google Scholar 

  6. Athitsos, V., Alon, J., Sclaroff, S., Kollios, G.: Boostmap: A method for efficient approximate similarity rankings. In: IEEE Conference on Computer Vision and Pattern Recognition (2004)

    Google Scholar 

  7. Gavrila, D.M., Philomin, V.: Real-time object detection for smart vehicles. In: IEEE International Conference on Computer Vision (1999)

    Google Scholar 

  8. Sudderth, E.B., Mandel, M.I., Freeman, W.T., Willsky, A.S.: Visual hand tracking using nonparametric belief propagation. In: IEEE CVPR Workshop on Generative Model Based Vision, vol. 12, p. 189 (2004)

    Google Scholar 

  9. Kato, M., Chen, Y.W., Xu, G.: Articulated hand tracking by pca-ica approach. In: International Conference on Automatic Face and Gesture Recognition, pp. 329–334 (2006)

    Google Scholar 

  10. Toyama, K., Blake, A.: Probabilistic tracking with exemplars in a metric space. International Journal of Computer Vision (2002)

    Google Scholar 

  11. Lin, Z., Davis, L.S., Doermann, D., DeMenthon, D.: Hierarchical part-template matching for human detection and segmentation. In: IEEE International Conference on Computer Vision (2007)

    Google Scholar 

  12. Olson, C.F., Huttenlocher, D.P.: Automatic target recognition by matching oriented edge pixels. IEEE Transactions on Image Processing (1997)

    Google Scholar 

  13. Thayananthan, A., Navaratnam, R., Stenger, B., Torr, P., Cipolla, R.: Pose estimation and tracking using multivariate regression. Pattern Recognition Letters (2008)

    Google Scholar 

  14. Stenger, B., Thayananthan, A., Torr, P.H.S., Cipolla, R.: Hand pose estimation using hierarchical detection. In: International Workshop on Human-Computer Interaction (2004)

    Google Scholar 

  15. Shaknarovich, G., Viola, P., Darrell, T.: Fast pose estimation with parameter-sensitive hashing. In: IEEE International Conference on Computer Vision (2003)

    Google Scholar 

  16. Lin, J.Y., Wu, Y., Huang, T.S.: 3D model-based hand tracking using stochastic direct search method. In: International Conference on Automatic Face and Gesture Recognition, p. 693 (2004)

    Google Scholar 

  17. Wu, Y., Lin, J.Y., Huang, T.S.: Capturing natural hand articulation. In: International Conference on Computer Vision, vol. 2, pp. 426–432 (2001)

    Google Scholar 

  18. Ouhaddi, H., Horain, P.: 3D hand gesture tracking by model registration. In: Workshop on Synthetic-Natural Hybrid Coding and Three Dimensional Imaging, pp. 70–73 (1999)

    Google Scholar 

  19. Amai, A., Shimada, N., Shirai, Y.: 3-d hand posture recognition by training contour variation. In: IEEE Conference on Automatic Face and Gesture Recognition, pp. 895–900 (2004)

    Google Scholar 

  20. Shimada, N., Kimura, K., Shirai, Y.: Real-time 3-d hand posture estimation based on 2-d appearance retrieval using monocular camera. In: IEEE International Conference on Computer Vision, p. 23 (2001)

    Google Scholar 

  21. Zhou, H., Huang, T.: Okapi-chamfer matching for articulated object recognition. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1026–1033 (2005)

    Google Scholar 

  22. Zhou, H., Huang, T.: Tracking articulated hand motion with eigen dynamics analysis. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1102–1109 (2003)

    Google Scholar 

  23. Stenger, B., Thayananthan, A., Torr, P.H.S., Cipolla, R.: Model-based hand tracking using a hierarchical bayesian filter. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1372–1384 (2006)

    Article  MATH  Google Scholar 

  24. Stenger, B.D.R.: Model-based hand tracking using a hierarchical bayesian filter. Dissertation submitted to the University of Cambridge (2004)

    Google Scholar 

  25. Wang, R.Y., Popović, J.: Real-time hand-tracking with a color glove. ACM Transactions on Graphics 28 (2009)

    Google Scholar 

  26. Mohr, D., Zachmann, G.: Fast: Fast adaptive silhouette area based template matching. In: Proceedings of the British Machine Vision Conference, pp. 39.1– 39.12. BMVA Press (2010), doi:10.5244/C.24.39

    Google Scholar 

  27. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Quasi-newton or variable metric methods in multidimensions. In: Numerical Recipes, The Art of Scientific Computing, pp. 521–526. Cambridge University Press, Cambridge (2007)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Clausthal University, Germany

    Daniel Mohr & Gabriel Zachmann

Authors
  1. Daniel Mohr
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  2. Gabriel Zachmann
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Editor information

Editors and Affiliations

  1. University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science and Engineering, University of South Carolina, 29208, Columbia, SC, USA

    Song Wang

  6. HRL Laboratories, 3011 Malibu Canyon Road, 90265-4797, Malibu, CA, USA

    Kim Kyungnam

  7. Purdue University, 47907-2021, West Lafayette, IN, USA

    Bedrich Benes

  8. Sandia National Laboratory, 87185, Albuquerque, NM, USA

    Kenneth Moreland

  9. University of Louisiana, 70504, Lafayette, LA, USA

    Christoph Borst

  10. Adobe Systems Incorporated, San Francisco, CA, USA

    Stephen DiVerdi

  11. Polytechnic Institute of NYU, 11201, Brooklyn, NY, USA

    Chiang Yi-Jen

  12. Lawrence Livermore National Laboratory, 94551-0808, Livermore, CA, USA

    Jiang Ming

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© 2011 Springer-Verlag Berlin Heidelberg

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Mohr, D., Zachmann, G. (2011). Segmentation-Free, Area-Based Articulated Object Tracking. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_11

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  • DOI: https://doi.org/10.1007/978-3-642-24028-7_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24027-0

  • Online ISBN: 978-3-642-24028-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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