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A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5815))

Abstract

Many real-time stereo vision systems are available on low-power platforms. They all either use a local correlation-like stereo engine or perform dynamic programming variants on a scan-line. However, when looking at high-performance global stereo methods as listed in the upper third of the Middlebury database, the low-power real-time implementations for these methods are still missing. We propose a real-time implementation of the semi-global matching algorithm with algorithmic extensions for automotive applications on a reconfigurable hardware platform resulting in a low power consumption of under 3W. The algorithm runs at 25Hz processing image pairs of size 750x480 pixels and computing stereo on a 680x400 image part with up to a maximum of 128 disparities.

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References

  1. Scharstein, D., Szeliski, R.: Middlebury stereo vision and evaluation page, http://vision.middlebury.edu/stereo

  2. Hirschmueller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: Proceedings of Int. Conference on Computer Vision and Pattern Recognition 2005, San Diego, CA, vol. 2, pp. 807–814 (June 2005)

    Google Scholar 

  3. Konolige, K.: Small vision systems. In: Proceedings of the International Symposium on Robotics Research, Hayama, Japan (1997)

    Google Scholar 

  4. Woodfill, J.I., et al.: The tyzx deepsea g2 vision system, a taskable, embedded stereo camera. In: Embedded Computer Vision Workshop, pp. 126–132 (2006)

    Google Scholar 

  5. Masrani, D.K., MacLean, W.J.: A real-time large disparity range stereo-system using fpgas. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3852, pp. 42–51. Springer, Heidelberg (2006)

    Google Scholar 

  6. Sabihuddin, S., MacLean, W.J.: Maximum-likelihood stereo correspondence using field programmable gate arrays. In: Int. Conference on Computer Vision Systems (ICVS), Bielefeld, Germany (March 2007)

    Google Scholar 

  7. Tech-News: Toyota’ lexus ls 460 employs stereo camera (viewed 2009/04/15), http://techon.nikkeibp.co.jp/english/NEWS_EN/20060301/113832/

  8. Sarnoff-Inc.: The acadia video processors - acadia pci (viewed 2009/07/15), http://www.sarnoff.com/products/acadia-video-processors/acadia-pci

  9. MobilEye: Eye q2 system - vision system on a chip (viewed 2009/07/15), http://www.mobileye.com/manufacturer-products/brochures

  10. Ernst, I., Hirschmüller, H.: Mutual information based semi-global stereo matching on the gpu. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 228–239. Springer, Heidelberg (2008)

    Google Scholar 

  11. Yang, Q.: Real-time global stereo matching using hierarchical belief propagation. In: British Machine Vision Conference (BMVC), pp. 989–998 (September 2006)

    Google Scholar 

  12. Hirschmueller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: Proceedings of Int. Conference on Computer Vision and Pattern Recognition 2007, Minneapolis, Minnesota (June 2007)

    Google Scholar 

  13. Hirschmueller, H., Gehrig, S.: Stereo matching in the presence of sub-pixel calibration errors. In: Proceedings of Int. Conference on Computer Vision and Pattern Recognition 2009, Miami, FL (June 2009)

    Google Scholar 

  14. Shimizu, M., Okutomi, M.: An analysis of subpixel estimation error on area-based image matching. In: DSP 2002, pp. 1239–1242 (2002)

    Google Scholar 

  15. Steingrube, P., Gehrig, S.: Performance evaluation of stereo algorithms for automotive applications. In: ICVS 2009 (October 2009)

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Gehrig, S.K., Eberli, F., Meyer, T. (2009). A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching. In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_14

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  • DOI: https://doi.org/10.1007/978-3-642-04667-4_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04666-7

  • Online ISBN: 978-3-642-04667-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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