Autonomous video surveillance and monitoring of human subjects in video has a rich history. Many deployed systems are able to reliably track human motion in indoor and controlled outdoor environments, e.g., parking lots and university campuses. A challenging domain of vital military importance is the surveillance of noncooperative and camouflaged targets within cluttered outdoor settings. These situations require both sensitivity and a very wide field of view and, therefore, are a natural application of omnidirectional video. Fundamentally, target finding is a change detection problem. Detection of camouflaged and adversarial targets implies the need for extreme sensitivity. Unfortunately, blind change detection in woods and fields may lead to a high fraction of false alarms, since natural scene motion and lighting changes produce highly dynamic scenes. Naturally, this desire for high sensitivity leads to a direct tradeoff between miss detections and false alarms. This paper discusses the current state of the art in video-based target detection, including an analysis of background adaptation techniques. The primary focus of the paper is the Lehigh Omnidirectional Tracking System (LOTS) and its components. This includes adaptive multibackground modeling, quasi-connected components (a novel approach to spatio-temporal grouping), background subtraction analyses, and an overall system evaluation.