<title>Target detection using image motion error measure</title>

Abstract

The spatio-temporal constraint equation for computation of the optical flow holds only over local spatio-temporal regions where motion is translational with constant velocity or over non-moving background regions where the image velocity is zero. Where the expression is true, it is possible to estimate the image motion vector of local image regions by minimizing the squared error over the local region. The expression is not true in the moving boundaries of moving objects over a stationary background, over regions with multiple moving objects, or over objects not in purely translational motion. Under these conditions, the accurate computation of motion vectors is not possible using this method. However, the error squared term, itself, may be used as a moving target indicator able to segment moving targets from noise and background clutter. This paper proposes and assesses the

<title>Reliable motion detection of small targets in video with low signal-to-clutter ratios</title>

“…Instead, Sandia's approach is to compute one iteration of a gradient descent to obtain a very coarse solution for the velocities of the image flow field. But rather than using these velocity values (V, , Vy) directly to indicate moving targets, Sandia's approach is to use the residual squared error energy of the equation in a small, localized region to indicate motion (Wayman 1993a(Wayman , 1993b. This is much more computationally efficient because the velocity values are not solved for with precision.…”