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.
Highlights
Advances in technology and economies of scale have made commercial digital video cameras a viable noncontact sensor for detecting vibrations on bridges, cars, hearts and lungs, equipment, and speech coupled to plastic bags [1]–[5]
Commercial video cameras digitally record light intensity emanating from a scene using a 2-D detector array sampled at a constant frame rate
A lower limit for subpixel motion estimation was proposed by Robinson and Milanfar [11]. Their results show that the Cramer–Rao lower bound (CRLB) for estimating subpixel motion is inversely proportional to the square of the gradient of the region of interest
Summary
Advances in technology and economies of scale have made commercial digital video cameras a viable noncontact sensor for detecting vibrations on bridges, cars, hearts and lungs, equipment, and speech coupled to plastic bags [1]–[5]. Commercial video cameras digitally record light intensity emanating from a scene using a 2-D detector array sampled at a constant frame rate These data can be processed using standard algorithms to detect vibrations of objects that occur at a rate that is below the Nyquist frequency associated with the frame rate of the camera [6]. Their results show that the Cramer–Rao lower bound (CRLB) for estimating subpixel motion is inversely proportional to the square of the gradient of the region of interest This result suggests that high-contrast targets with large gradients should be used to maximize detection results. A bound for detecting subpixel motion in digital video cameras based on quantization noise has been developed by Uss et al [12].
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