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.
- }}K. Ambrosch. Mapping Stereo Matching Algorithms to Hardware. PhD thesis, Vienna University of Technology, 2009.Google Scholar
- }}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 ScholarCross Ref
- }}S. Birchfield and C. Tomasi. Depth discontinuities by pixel-to-pixel stereo. International Journal of Computer Vision, 35:3:269--293, 1996. Google ScholarDigital Library
- }}J. Y. Bouguet. Camera Calibration Toolbox for Matlab, 2008. http://www.vision.caltech.edu/bouguetj/calib_doc.Google Scholar
- }}G. Bradski and A. Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly, Cambridge, MA, 2008.Google Scholar
- }}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 ScholarDigital Library
- }}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 ScholarCross Ref
- }}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 Scholar
- }}P. F. Felzenszwalb and D. P. Huttenlocher. Efficient belief propagation for early vision. Int. J. Comput. Vision, 70(1):41--54, 2006. Google ScholarDigital Library
- }}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 ScholarDigital Library
- }}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 ScholarDigital Library
- }}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 ScholarCross Ref
- }}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 ScholarDigital Library
- }}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 ScholarDigital Library
- }}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 ScholarCross Ref
- }}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 ScholarDigital Library
- }}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 ScholarDigital Library
- }}M. Sonka, V. Hlavac, and R. Boyle. Image Processing, Analysis, and Machine Vision. Thomson-Engineering, 2 edition, 1999.Google Scholar
- }}Texas Instruments. TMS320C6414T, TMS320C6415T, TMS320C6416T Fixed-Point Digital Signal Processors, 2003. Lit. Number: SPRS226K.Google Scholar
- }}VidereDesign. Stereo-on-a-Chip Stereo Head User Manual 1.3. Videre Design, 2007. http://www.videredesign.com/vision/stoc.htm.Google Scholar
- }}Vision Components GmbH. VC4XXX Operating Manual, 2008. Document name: VC4XXX_HW.pdf.Google Scholar
- }}Vision Components GmbH. VCRT 5.0 Software Manual, 2008. Document name: VCRT5.pdf.Google Scholar
- }}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 ScholarDigital Library
- }}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 ScholarDigital Library
- }}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 ScholarCross Ref
- }}J. S. Yedidia, W. T. Freeman, and Y. Weiss. Understanding belief propagation and its generalizations. pages 239--269, 2003. Google ScholarDigital Library
- }}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 ScholarDigital Library
- }}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 ScholarCross Ref
- }}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 ScholarDigital Library
- }}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 ScholarDigital Library
- }}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 ScholarDigital Library
Index Terms
- Distributed real-time stereo matching on smart cameras
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