Common visual cues for sports highlights modeling

Automatic annotation of semantic events allows effective retrieval of video content. In this work, we present solutions for highlights detection in sports videos. The proposed approach exploits the typical structure of a wide class of sports videos, namely those related to sports which are played in delimited venues with playfields of well known geometry, like soccer, basketball, swimming, track and field disciplines, and so on. For these sports, a modeling scheme based on a limited set of visual cues and on finite state machines that encode the temporal evolution of highlights is presented, that is of general applicability to this class of sports. Visual cues encode position and speed information coming from the camera and from the object/athletes that are present in the scene, and are estimated automatically from the video stream. Algorithms for model checking and for visual cues estimation are discussed, as well as applications of the representation to different sport domains.