抄録
Real-time full-pixel optical flow estimation is conducted for 512 x 512 images by accelerating the Lucas-Kanade method on a CPU-based high-speed vision system. Our system can remarkably expand the measurable range of the estimate flows by implementing a novel algorithm that can optimally select frame intervals and space intervals in optical flow estimation according to the estimated flow speed. Its effectiveness is verified by showing estimated motion distributions for high-speed objects moving at 100 km/h or more.
