An Investigation into Optical Flow Computation on FPGA Hardware

Motion estimation (or optical flow estimation) is an interesting problem in computer vision. However this task is computationally intensive for conventional processors. In this work an investigation into an FPGA-based hardware architecture for real-time motion estimation is carried out. The algorithm used in this paper is a gradient based inverse finite element method for optical flow computation. It carries out the motion estimation by calculating the gradient, Laplacian, and subsequently the velocities of each pixel in a parallel design which aids in the speed of the computation. The algorithm has been benchmarked against many of the well known algorithms cited within the field. A literature review is carried out and the algorithm is discussed. An FPGA design is presented. Preliminary simulation results are shown and discussed.