Motion Entropy Feature and Its Applications to Event-Based Segmentation of Sports Video
Chen-Yu Chen, Jia‐Ching Wang, Jhing-Fa Wang, Yu Hen Hu · EURASIP Journal on Advances in Signal Processing · 2008
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.