US Patent Patent# 9.361.706 was awarded on 06/07/2016.

Many computer vision applications require real-time processing of image data.  This requirement is especially critical for autonomous vehicles performing obstacle avoidance, path planning, and target tracking tasks.  A quickly calculated and relatively rough motion estimate is more useful for autonomous navigation than a more accurate, but slowly calculated estimate. Recent technology advancements in small unmanned air and ground vehicles make many low-cost surveillance and military applications possible. Most of these applications demand a low power, compact, light weight, and high speed computation platform for processing image data in real time. In most cases, the traditional general purpose processor and sequentially executed software approach does not meet these requirements.  In this research, a tensor-based optical flow algorithm is modified and implemented using field programmable gate array (FPGA) for small unmanned vehicle obstacle avoidance and navigation. 

Graduate Students:

  Zhaoyi Wei

1. Real-Time Optical Flow Sensor Design And Its Application To Obstacle Detection                      Patent: 12,651,907
2. Z.Y. Wei, D.J. Lee, B.E. Nelson, and J.K Archibald, “Hardware-Friendly Vision Algorithms for Embedded Obstacle Detection Applications,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 20/11, p. 1577-1589, November 2010.

3. Z.Y. Wei, D.J. Lee, and B.E. Nelson, “A Hardware Friendly Adaptive Tensor-based Optical Flow Algorithm,” Lecture Notes in Computer Science (LNCS), Part II, LNCS 4842, p. 43-51, International Symposium on Visual Computing (ISVC), Lake Tahoe, CA, U.S.A., November 26-28, 2007.

4. Z.Y. Wei, D.J. Lee, and B.E. Nelson, “FPGA-based Real-time Optical Flow Algorithm Design and Implementation,” Journal of Multimedia, vol. 2/5, p. 38-45, September 2007.

5. Z. Wei, D.J. Lee, B.E. Nelson, and M.A. Martineau, “A fast and accurate tensor-based optical flow algorithm implemented in FPGA”, IEEE Workshop on Applications of Computer Vision (WACV 2007), Austin, Texas, USA, Feb 21-22, 2007.