skip to main content
10.1145/1865987.1866016acmconferencesArticle/Chapter ViewAbstractPublication PagesicdscConference Proceedingsconference-collections
research-article

Distributed real-time stereo matching on smart cameras

Published:31 August 2010Publication History

ABSTRACT

This work introduces a real-time capable realization of an area-based stereo matching algorithm that is distributed on two embedded smart camera platforms. Combining common industrial smart cameras by this way enables real time stereo vision as a new application domain for these platforms. With the proposed method, the computational load can be shared among the two cameras equipped with a digital signal processor each. This results in an efficient processing of a computational intensive stereo matching algorithm--- the processing speed is significantly faster compared to a single chip solution. Beside that, various optimizations especially developed for digital signal processors additionally increase the performance. On input images of 450×375 and a disparity range of 60, the system achieves a stereo processing performance of 11.8 frames per second. The stereo matching quality is evaluated using the Middlebury stereo database where it is the only purely embedded algorithm.

References

  1. }}K. Ambrosch. Mapping Stereo Matching Algorithms to Hardware. PhD thesis, Vienna University of Technology, 2009.Google ScholarGoogle Scholar
  2. }}A. Banno and K. Ikeuchi. Disparity map refinement and 3d surface smoothing via directed anisotropic diffusion. In Proceedings of the IEEE 12th International Conference on Computer Vision Workshops, pages 1870--1877, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  3. }}S. Birchfield and C. Tomasi. Depth discontinuities by pixel-to-pixel stereo. International Journal of Computer Vision, 35:3:269--293, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. }}J. Y. Bouguet. Camera Calibration Toolbox for Matlab, 2008. http://www.vision.caltech.edu/bouguetj/calib_doc.Google ScholarGoogle Scholar
  5. }}G. Bradski and A. Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly, Cambridge, MA, 2008.Google ScholarGoogle Scholar
  6. }}M. Z. Brown, D. Burschka, and G. D. Hager. Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25:993--1008, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. }}N. Chang, T.-M. Lin, T.-H. Tsai, Y.-C. Tseng, and T.-S. Chang. Real-time dsp implementation on local stereo matching. In Proc. IEEE International Conference on Multimedia and Expo, pages 2090--2093, 2--5 July 2007.Google ScholarGoogle ScholarCross RefCross Ref
  8. }}O. Faugeras, B. Hotz, H. Mathieu, T. Vieville, Z. Zhang, P. Fua, E. Theron, L. Moll, G. Berry, J. Vuillemin, P. Bertin, and C. Proy. Real-time correlation based stereo: algorithm implementations and applications. Technical Report 2013, INRIA, 1993.Google ScholarGoogle Scholar
  9. }}P. F. Felzenszwalb and D. P. Huttenlocher. Efficient belief propagation for early vision. Int. J. Comput. Vision, 70(1):41--54, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. }}S. Forstmann, Y. Kanou, J. Ohya, S. Thuering, and A. Schmitt. Real-time stereo by using dynamic programming. In Conference on Computer Vision and Pattern Recognition Workshop, page 29. IEEE Computer Society, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. }}H. Hirschmueller. Accurate and efficient stereo processing by semi-global matching and mutual information. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. }}M. Humenberger, C. Zinner, and W. Kubinger. Performance evaluation of a census-based stereo matching algorithm on embedded and multi-core hardware. In Proceedings of the International Symposium on Image and Signal Processing and Analysis, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  13. }}M. Humenberger, C. Zinner, M. Weber, W. Kubinger, and M. Vincze. A fast stereo matching algorithm suitable for embedded real-time systems. To appear in Journal on Computer Vision and Image Understanding, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. }}T. Kanade, A. Yoshida, K. Oda, H. Kano, and M. Tanaka. A stereo machine for video-rate dense depth mapping and its new applications. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 196--202, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. }}B. Khaleghi, S. Ahuja, and Q. Wu. An improved real-time miniaturized embedded stereo vision system (mesvs-ii). In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPR Workshops 2008, pages 1--8, 23--28 June 2008.Google ScholarGoogle ScholarCross RefCross Ref
  16. }}V. Kolmogorov and R. Zabih. Multi-camera scene reconstruction via graph cuts. In Proceedings of the European Conference on Computer Vision, pages 82--96, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. }}D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1--3):7--42, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. }}M. Sonka, V. Hlavac, and R. Boyle. Image Processing, Analysis, and Machine Vision. Thomson-Engineering, 2 edition, 1999.Google ScholarGoogle Scholar
  19. }}Texas Instruments. TMS320C6414T, TMS320C6415T, TMS320C6416T Fixed-Point Digital Signal Processors, 2003. Lit. Number: SPRS226K.Google ScholarGoogle Scholar
  20. }}VidereDesign. Stereo-on-a-Chip Stereo Head User Manual 1.3. Videre Design, 2007. http://www.videredesign.com/vision/stoc.htm.Google ScholarGoogle Scholar
  21. }}Vision Components GmbH. VC4XXX Operating Manual, 2008. Document name: VC4XXX_HW.pdf.Google ScholarGoogle Scholar
  22. }}Vision Components GmbH. VCRT 5.0 Software Manual, 2008. Document name: VCRT5.pdf.Google ScholarGoogle Scholar
  23. }}J. Woodfill and B. V. Herzen. Real-time stereo vision on the parts reconfigurable computer. In IEEE Symposium on FPGAs for Custom Computing Machines, pages 242--250. IEEE Computer Society Press, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. }}J. I. Woodfill, G. Gordon, D. Jurasek, T. Brown, and R. Buck. The Tyzx DeepSea G2 Vision System, A Taskable, Embedded Stereo Camera. In Proceedings of the 2006 Conference on Computer Vision and Pattern Recoginition Workshops, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. }}Q. Yang, L. Wang, R. Yang, S. Wang, M. Liao, and D. Nister. Real-time global stereo matching using hierarchical belief propagation. In Proceedings of The British Machine Vision Conference, pages 989--998, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  26. }}J. S. Yedidia, W. T. Freeman, and Y. Weiss. Understanding belief propagation and its generalizations. pages 239--269, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. }}R. Zabih and J. Woodfill. Non-parametric local transforms for computing visual correspondence. In Proceedings of 3rd European Conf. Computer Vision, pages 151--158, Stockholm, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. }}Z. Zhang. Flexible camera calibration by viewing a plane from unknown orientations. In Proc. Seventh IEEE International Conference on Computer Vision The, volume 1, pages 666--673, 20--27 Sept. 1999.Google ScholarGoogle ScholarCross RefCross Ref
  29. }}C. Zinner, M. Humenberger, K. Ambrosch, and W. Kubinger. An optimized software-based implementation of a census-based stereo matching algorithm. In Advances in Visual Computing, Lecture Notes in Computer Science, volume 5358, pages 216--227. Springer, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. }}C. Zinner and W. Kubinger. ROS-DMA: A DMA double buffering method for embedded image processing with resource optimized slicing. In Proc. 12th IEEE Real-Time and Embedded Technology and Applications Symposium, pages 361--372, 04--07 April 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. }}C. Zinner, W. Kubinger, and R. Isaacs. Pfelib: A performance primitives library for embedded vision. EURASIP Journal on Embedded Systems, 2007(1):14, 1 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Distributed real-time stereo matching on smart cameras

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        ICDSC '10: Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
        August 2010
        252 pages
        ISBN:9781450303170
        DOI:10.1145/1865987

        Copyright © 2010 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 31 August 2010

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate92of117submissions,79%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader