Event-Based Segmentation of Sports Video Using Motion Entropy

Abstract

An event-based segmentation method for sports videos is presented. A motion entropy criterion is employed to characterize the level of intensity of relevant object motion in individual frames of a video sequence. The resulting motion entropy curve then is approximated with a piece-wise linear model using a homoscedastic error model based time series change point detection algorithm. It is observed that interesting sports events are correlated with specific patterns of the piece-wise linear model. A set of empirically derived classification rules then is derived based on these observations. Application of these rules to the motion entropy curve leads to this motion entropy curve, one is able to segment the corresponding video sequence into individual sections, each consisting of a semantically relevant event. The proposed method is tested on six hours of sports videos including basketball, soccer and tennis. Excellent experimental results are observed.