Name
Abstract | ||
|---|---|---|
The paper focuses on motion-based information extraction from cluttered video image sequences. A novel method is introduced which can reliably detect walking human figures contained in such images. The method works with spatio-temporal input information to detect and classify patterns typical of human movement. Our algorithm consists of real-time operations, which is an important factor in practical applications. The paper presents a new information-extraction and temporal tracking method based on a simplified version of the symmetry-pattern extraction, which pattern is characteristic for the moving legs of a walking person. These spatio-temporal traces are labelled by kernel Fisher discriminant analysis. With the use of temporal tracking and non-linear classification we have achieved pedestrian detection from cluttered image scenes with a correct classification rate of 97.6% from 1 to 2 step periods. The detection rates of linear classifier and SVM are also presented in the results hereby the necessity of a non-linear method and the power of KFDA for this detection task is also demonstrated. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.patrec.2005.11.009 | Pattern Recognition Letters |
Keywords | Field | DocType |
suicide prevention,higher order,tracking,information extraction,human factors,kernel fisher discriminant analysis,gait analysis,injury prevention,occupational safety,ergonomics | Computer vision,Nonlinear system,Pattern recognition,Support vector machine,Kernel Fisher discriminant analysis,Information extraction,Artificial intelligence,Linear classifier,Accident prevention,Pedestrian detection,Classification rate,Mathematics | Journal |
Volume | Issue | ISSN |
27 | 7 | 0167-8655 |
Citations | PageRank | References |
12 | 1.02 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laszlo Havasi | 1 | 66 | 6.80 |
| Zoltán Szlávik | 2 | 116 | 21.40 |
| Sziranyi, T. | 3 | 395 | 44.76 |