Name
Abstract | ||
|---|---|---|
This paper proposes a novel and robust appearance-based method for human motion recognition based on the eigenspace technique. This method has three main advantages over the existing appearance-based methods. First, the Linear Discriminant Analysis (LDA) is used for dimensionality reduction and eigenspace generation, while preserving maximum separability between classes. Second, by combining a novel centering technique with an incremental procedure, the motion data becomes more concise, expressive, and less confused. Third, data storage is greatly enhanced by using a directed acyclic graph (DAG) structure based on Euclidean distance between projected data. The method is rigorously trained and tested using KTH dataset which contains a large number of motion videos partitioned into six human motions. The experimental results are very promising yielding an average recognition rate of 94.17%. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13772-3_18 | ICIAR |
Keywords | Field | DocType |
average recognition rate,human motion,motion data,robust appearance-based method,human motion recognition,existing appearance-based method,novel human motion recognition,projected data,motion video,eigenspace generation,data storage,euclidean distance,directed acyclic graph | Computer vision,Dimensionality reduction,Pattern recognition,Computer science,Computer data storage,Euclidean distance,Directed acyclic graph,Human motion,Artificial intelligence,Linear discriminant analysis,Eigenvalues and eigenvectors | Conference |
Volume | ISSN | ISBN |
6111 | 0302-9743 | 3-642-13771-7 |
Citations | PageRank | References |
5 | 0.43 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdunnaser Diaf | 1 | 7 | 1.49 |
| Riadh Ksantini | 2 | 82 | 15.39 |
| Boubakeur Boufama | 3 | 162 | 22.02 |
| Rachid Benlamri | 4 | 135 | 23.55 |