Abstract
An optical flow is defined as a motion vector for each pixel between successive two images in a video. This paper proposes a fast optical flow algorithm with robustness for luminance change. The algorithm includes the luminance change estimation, initial vector decision, fast hierarchical processing, and window size optimization. These methods result in the robustness for the luminance change, accurate flow estimation, and low computational costs. Simulation results show the flow accuracy improves from 18.65° to 4.29°, when giving luminance change. The CPU execution time reduces to 15% compared to the conventional with the same accuracy. In addition, we propose a dedicated architecture based on this algorithm and implement it on an FPGA. Experimental results show the new architecture decreases the operating frequency to 20%, the circuit area to 45%, and the power consumption to 9%. This circuit is applicable to real-time image recognition and image reconstruction.
