Motion Entropy Feature and Its Applications to Event-Based Segmentation of Sports Video. (6th August 2008) - Chen Chen, Chen-Yu Chen-Yu; Wang Wang, Jia-Ching Jia-Ching; Wang Wang, Jhing-Fa Jhing-Fa; Hu Hu, Yu-Hen Yu-Hen
Abstract : An entropy-based criterion is proposed to characterize the pattern and intensity of object motion in a video sequence as a function of time. By applying a homoscedastic error model-based time series change point detection algorithm 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.