Sports highlight recognition and event detection using rule inference system

Abstract

Computer vision in sport is a very interesting application. People spend a lot of time watching sports videos because this is one of the best field of entertainment. Sports video broadcasts generally take a lot of time, ranging from two to four hours. However, the interesting part happens for just a few minutes. Detecting the highlighted event in a sport will be useful for people who like to watch only the prominent events section instead of watching the whole video broadcast. Event detection will give precise details about the action that occurred for a particular time, but the detection of highlighted events is more complex. This is due to the fact that a sports video contains collections of events. Among them, segregation of the required event is a time-consuming process but it requires more knowledge about the sport as well as processing time. Hence, a novel work is proposed focused on identifying the location of the functional object using agglomerative clustering and annotating the event highlights automatically by means of the rule inference mechanism. The SHRED (Sports Highlight Recognition and Event Detection) system achieves an overall accuracy of about 97.38% relative to other state-of-art methods in event class annotation.