Subpixel motion detection using a commercial video camera

Abstract

Performance metrics for detecting subpixel motion are calculated for a commercial video camera using image difference data processed with three Neyman-Pearson-based algorithms. High-signal-to-noise-ratio data are collected and analyzed for a thin black bar that slowly oscillates against a white background. The position and velocity of the bar are estimated using Fourier-based processing techniques. The probability of detecting subpixel motion as a function of false alarm rate, number of pixels tested, subpixel shift, and detection algorithm are calculated with Monte Carlo simulations using the experimental data. The results characterize the best performance curves for detecting subpixel motion for most commercial video cameras and targets.