Name
Title | ||
|---|---|---|
Motion entropy feature and its applications to event-based segmentation of sports video |
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1155/2008/460913 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
motion entropy feature,corresponding video sequence,entropy-based criterion,motion entropy curve,event-based segmentation,excellent experimental result,sports video,object motion,model-based time series change,homoscedastic error,video sequence | Computer vision,Block-matching algorithm,Change detection,Computer science,Segmentation,Homoscedasticity,Image processing,Image segmentation,Artificial intelligence,Image sequence,Basketball | Journal |
Volume | Issue | ISSN |
2008, | 1 | 1687-6180 |
Citations | PageRank | References |
9 | 0.64 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen-Yu Chen | 1 | 52 | 6.35 |
| Jia-Ching Wang | 2 | 515 | 58.13 |
| Jhing-fa Wang | 3 | 982 | 114.31 |
| Yu-Hen Hu | 4 | 828 | 66.51 |