Accuracy of Gradient-Based Optical Flow Estimation in High-Frame-Rate Video Analysis

Abstract

This study investigates the effect of frame intervals on the accuracy of the Lucas-Kanade optical flow estimates for high-frame-rate (HFR) videos, with a view to realizing accurate HFR-video-based optical flow estimation. For 512×512 pixels videos of patterned objects moving at different speeds and captured at 1000frames per second, the averages and standard deviations of the estimated optical flows were determined as accuracy measures for frame intervals of 1-40ms. The results showed that the accuracy was highest when the displacement between frames was around 0.6pixel/frame. This common property indicates that accurate optical flow estimation for HFR videos can be realized by varying frame intervals according to the motion field: a small frame interval for high-speed objects and a large frame interval for low-speed objects.