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     Accurate real-time motion estimation is very critical to many computer vision tasks. However, because of its computational power and processing speed requirements, it is rarely used for real-time applications, especially for micro unmanned vehicles. In our previous work, a FPGA system was built to process optical flow vectors of 64 frames of 640480 image per second. Compared to software-based algorithms, this system achieved much higher frame rate but marginal accuracy. In this paper, a more accurate optical flow algorithm is proposed. Temporal smoothing is incorporated in the hardware structure which significantly improves the algorithm accuracy. To accommodate temporal smoothing, the hardware structure is composed of two parts: the derivative (DER) module produces intermediate results and the optical flow computation (OFC) module calculates the final optical flow vectors. Software running on a built-in processor on the FPGA chip is used in the design to direct the data flow and manage hardware components. This new design has been implemented on a compact, low power, high performance hardware platform for micro UV applications.  It is able to process 15 frames of 640480 image per second and with much improved accuracy. Higher frame rate can be achieved with further optimization and additional memory space.

 Project Sponsors:

  David and Deborah Huber Scholarship



 Graduate Students:

  Zhaoyi Wei

  1. J.M. Bodily, B.E. Nelson, Z.Y. Wei, D.J. Lee, and J. Chase, “A Comparison Study On Implementing Optical Flow and Digital Communications on FPGAs and GPUs,” ACM Transactions on Reconfigurable Technology and Systems, vol. 3/2, Article 6, 22 pages, May 2010.

  2. Z.Y. Wei, D.J. Lee, B.E. Nelson, and J.K. Archibald, “Real-time Accurate Optical Flow-based Motion Sensor,” IEEE International Conference on Pattern Recognition (ICPR), Tampa, FL, USA, p. 1-4, doi: 10.1109/ICPR.2008.4761126, December 8-11, 2008.

  3. Z.Y. Wei, D.J. Lee, B.E. Nelson, J.K. Archibald, and B.B. Edwards, "ʺFPGA-Based Embedded Motion Estimation Sensor," International Journal of Reconfigurable Computing, vol. 2008, Article ID 636145, 8 pages, July 2008.
  4. J. Chase, B.E. Nelson, J.M. Bodily, Z.Y. Wei, and D.J. Lee, “FPGA and GPU Architectures For Real-Time Optical Flow Calculations,”, IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM), p. 173-182, Palo Alto, CA, USA, April 14-15, 2008.

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