Two methods for detecting dim, unresolved target tracks in infrared imagery are presented. Detecting such targets in a sequence of noisy images is very challenging from the standpoint of algorithm design as well as detection performance evaluation. Since the signal-to-noise ratio per pixel is very low (a dim target) and the target is unresolved (of spatial extent less than a pixel), one must rely on integration over target tracks which span over many image frames. In addition, since there is a large amount of uncertainty as to the pattern and location of target tracks, good algorithms must consider a large number of possibilities. The first method is based on a generalization of the Hough transform-based algorithm using the Radon transform. The second approach is an extension of a detection theory algorithm to 3-D. Both algorithms use a 3-D volume of spatial-temporal data.