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.
Highlights
Automated video skimming and summarization represent a hotly pursued research topic in the field of contentbased video analysis
The proposed event detection approach detects a set of significant event segments from the video
The time series entropy motion value (EMV) is segmented by the change point detection module
Summary
Automated video skimming and summarization represent a hotly pursued research topic in the field of contentbased video analysis. Based on this observation, we are motivated to investigate the feasibility of using motion pattern and intensity to characterize potentially semantically significant events in a sport video It has been observed [12] that global camera motion often causes excessive false alarm when the dominant motion value is used as a feature to analyze a sports video. Using a maximum-likelihood estimation method based on the homoscedastic error model [13], this time function is approximated by a series of piecewise linear line segments, jointed by change points By examining this approximated motion entropy function against the original sports video, it is observed that semantically meaningful sports events are often highly synchronized with certain change patterns by which the video sequence can be divided.
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