Near-duplicate video clip (NDVC) detection is an important problem with a wide range of applications such as TV broadcast monitoring, video copyright enforcement, content-based video clustering and annotation, etc. This system performs online NDVC detection and comprises two novel complementary schemes for detecting NDVCs. The first globally summarizes a video to a single vector which captures the dominating content and content changing trends of each clip. The second maps each clip to a sequence of symbols, and takes temporal order and sequence context information into consideration. It has demonstrated real-time NDVC detection with high accuracy.