The largest challenge with all persistent surveillance systems is they require a trade between area coverage and ground object resolution. This trade typically results in provision of imagery where objects desired to be tracked have a small total number of pixels (often less than a few hundred total). With such low pixel counts, traditional target recognition methods become difficult. For this reason, most persistent surveillance tracking systems are based on detection and tracking of image changes. These change-detection tracking systems, however, struggle to maintain tracks through quick maneuvers, stops, obscurations, and dense traffic. Feature descriptors, including template matching, histogram of oriented gradients (HOG), and local binary patterns (LBP) are evaluated for use in the special case of very low pixel count target detection and track maintenance. These dynamic feature-based detection models are incorporated into a change-detection based tracking system. The resulting composite tracking system will be described as applied to EO and MWIR wide area data collected under a variety of conditions. Resulting tracking system improvements and tradeoffs between feature descriptors are presented.