Name
Abstract | ||
|---|---|---|
In this article, we propose a novel framework for robust visual behavior understanding, capable of achieving high recognition rates in demanding real-life environments and in almost real time. Our approach is based on the utilization of holistic visual behavior understanding methods, which perform modeling directly at the pixel level. This way, we eliminate the world representation layer that can ... |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/MSP.2010.937392 | IEEE Signal Processing Magazine |
Keywords | Field | DocType |
Hidden Markov models,Feature extraction,Data models,Cameras,Real time systems,Visualization,Pixel | Computer vision,Data modeling,Activity recognition,Visualization,Computer science,Feature extraction,Exploit,Pixel,Behavior recognition,Artificial intelligence,Hidden Markov model,Machine learning | Journal |
Volume | Issue | ISSN |
27 | 5 | 1053-5888 |
Citations | PageRank | References |
29 | 1.06 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitrios I. Kosmopoulos | 1 | 378 | 27.91 |
| Sotirios P. Chatzis | 2 | 250 | 24.25 |