Object tracking in wide area motion imagery is a complex problem that consists of object detection and target tracking over time. This challenge can be solved by human analysts who naturally have the ability to keep track of an object in a scene. A computer vision solution for object tracking has the potential to be a much faster and efficient solution. However, a computer vision solution faces certain challenges that do not affect a human analyst. To overcome these challenges, a tracking process is proposed that is inspired by the known advantages of a human analyst. First, the focus of a human analyst is emulated by doing processing only the local object search area. Second, it is proposed that an intensity enhancement process should be done on the local area to allow features to be detected in poor lighting conditions. This simulates the ability of the human eye to discern objects in complex lighting conditions. Third, it is proposed that the spatial resolution of the local search area is increased to extract better features and provide more accurate feature matching. A quantitative evaluation is performed to show tracking improvement using the proposed method. The three databases, each grayscale sequences that were obtained from aircrafts, used for these evaluations include the Columbus Large Image Format database, the Large Area Image Recorder database, and the Sussex database.