In this research, a transition effect detection scheme for identifying possible highlight segments in baseball videos will be presented. The effects that are inserted manually by the broadcasters for signaling the slow-motion segments will be extracted and the frames containing such effects can serve as anchor positions for further processing. A set of video segments will first be chosen to construct the 'transition effect template' for the archived video. The candidate frames will be compared with this template for searching the slow-motion video segments. In baseball videos, we further construct the 'pitching view template' so that the starting positions of the video segments of interest can be located. By processing these segments only, we may further employ such method as hidden Markov model to classify their content. The major contribution of this research is the usage of compressed-domain features to achieve the efficiency. The experimental results show the feasibility of the proposed scheme.