Real-time view recognition and event detection for sports video
In this paper, we present a general framework and new effective algorithms to detect the syntactic structures that are at a level higher than shots. In sports video, such high-level structures are often characterized by the specific views (e.g., pitching or serve) and the subsequent temporal transition patterns within each temporal structural segment. We have developed robust statistical models for detecting the domain-specific views with real-time performance and high accuracy. The models combine domain-independent global color filtering method and domain-specific constraints on the spatio-temporal properties of the segmented regions (e.g., locations, shapes, and motion of the objects). The real-time performance was accomplished by using efficient compressed-domain processing at the front end and computational expensive object-level processing on filtered candidates only. High-level events (e.g., strokes, net plays, and baseline plays) are also detected after the view recognition. Results of such structure and event detection allow for efficient browsing and summarization of long sports video programs.
video indexing content-based video retrieval sports video analysis mpeg-7