Name
Abstract | ||
|---|---|---|
Accurate background/foreground segmentation is a preliminary process essential to most visual surveillance applications. With the increasing use of freely moving cameras, strategies have been proposed to refine initial segmentation. In this paper, it is proposed to exploit the Vide-omics paradigm, and Profile Hidden Markov Models in particular, to create a new type of object descriptors relying on spatiotemporal information. Performance of the proposed methodology has been evaluated using a standard dataset of videos captured by moving cameras. Results show that usage of the proposed object descriptors allows better foreground extraction than standard approaches. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Computer vision, Visual Surveillance, Foreground detection, Freely Moving Cameras, Vide-omics |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Visualization,Segmentation,Computer science,Object model,Image segmentation,Exploit,Artificial intelligence,Hidden Markov model,Visual surveillance | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioannis Kazantzidis | 1 | 0 | 0.34 |
| Francisco Flórez-revuelta | 2 | 481 | 34.95 |
| Jean-christophe Nebel | 3 | 238 | 19.58 |