As more and more visual information is available on video, information indexing and retrieval of digital video data is becoming important. A digital video database embedded with visual information processing using image analysis and image understanding techniques such as automated target detection, classification, and identification can provide query results of higher quality. We address in this paper a robust digital video database system within which a target detection module is implemented and applied onto the keyframe images extracted by our digital library system. The tasks and application scenarios under consideration involve indexing video with information about detection and verification of artificial objects that exist in video scenes. Based on the scenario that the video sequences are acquired by an onboard camera mounted on Predator unmanned aircraft, we demonstrate how an incoming video stream is structured into different levels -- video program level, scene level, shot level, and object level, based on the analysis of video contents using global imagery information. We then consider that the keyframe representation is most appropriate for video processing and it holds the property that can be used as the input for our detection module. As a result, video processing becomes feasible in terms of decreased computational resources spent and increased confidence in the (detection) decisions reached. The architecture we proposed can respond to the query of whether artificial structures and suspected combat vehicles are detected. The architecture for ground detection takes advantage of the image understanding paradigm and it involves different methods to locate and identify the artificial object rather than nature background such as tree, grass, and cloud. Edge detection, morphological transformation, line and parallel line detection using Hough transform applied on key frame images at video shot level are introduced in our detection module. This function can also help rapidly filter incoming video and extract only those video sequences of potential interest under real time combating environment. Experimental results on video sequences acquired by Predator prove the feasibility of our approach.