Scale Space algorithms, most notably the Scale Invariant Feature Transform (SIFT) are used to produce robust local features from a single training image or a small number of samples. These features can then be computed from, and matched to, test imagery. An object detection/recognition algorithm is built around this matching process. The aim of this project is to locate a particular class of side-attack landmines, PARMs, in SWIR video using a SIFT-based detector. The detection of PARMs is accomplished by generating SIFT keypoints for the training images and each frame of the video and then determining a model of keypoint matches that represent the scale, orientation, and location of PARMs in the video with a high degree of certainty. Once a SIFT match between the training image set and a frame of video is found, a new hypothesis about the location, relative orientation, and scale of a PARM in the video sequence is created. Each new keypoint match is then assigned a confidence-based score. If this new keypoint match is compatible with the geometrical model of any previously generated hypothesis, the keypoint match's score, scaled by a fuzzy membership function, is added to the score of that hypothesis. An alarm is triggered once the score of a hypothesis reaches a predefined threshold. Results of the side-attack landmine detection are presented.