Soccer event detection deals with identifying interesting segments in soccer video via audio/visual content analysis. This task enables automatic high-level index creation, which circumvents large-scale manual annotation and facilitates semantic-based retrieval. This thesis proposes two frameworks for event detection through collaborative analysis of textual, aural and visual
features. The frameworks share a common initial component where both utilize an external textual resource, which is the minute-by-minute (MBM) reports from sports broadcasters, to accurately localize sections of video containing the desired events
