In this paper, we develop a high-frame-rate (HFR) vision system that can estimate the optical flow in real time at 1000 f/s for 1024×1024 pixel images via the hardware implementation of an improved optical flow detection algorithm on a high-speed vision platform. Based on the Lucas-Kanade method, we adopt an improved gradient-based algorithm that can adaptively select a pseudo-variable frame rate according to the amplitude of the estimated optical flow to accurately detect the optical flow for objects moving at high and low speeds in the same image. The performance of our developed HFR optical flow system was verified through experimental results for high-speed movements such as a top's spinnning motion and a human's pitching motion.
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