Name
Abstract | ||
|---|---|---|
The obtaining of perfect foreground segmentation masks still remains as a challenging task in video surveillance systems, since errors in that initial stage could lead to misleadings in subsequent tasks as object tracking and behavior analysis. This work presents a novel methodology based on self-organizing neural networks and Gaussian distributions to detect unusual objects in the scene, and to improve the foreground mask handling occlusions between objects. After testing the proposed approach on several traffic sequences obtained from public repositories, the results demonstrate that this methodology is promising and suitable to correct segmentation errors on crowded scenes with rigid objects. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s00500-014-1575-3 | Soft Computing |
Keywords | Field | DocType |
Self-organizing neural networks, Postprocessing techniques, Traffic monitoring, Surveillance systems, Object detection | Object detection,Computer vision,Monitoring system,Segmentation,Computer science,Vehicle detection,Self-organizing map,Video tracking,Gaussian,Artificial intelligence,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
19 | 9 | 1433-7479 |
Citations | PageRank | References |
6 | 0.46 | 33 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael Marcos Luque-Baena | 1 | 96 | 13.24 |
| Ezequiel López-Rubio | 2 | 323 | 39.73 |
| Enrique Domínguez | 3 | 133 | 21.24 |
| Esteban J. Palomo | 4 | 95 | 14.79 |
| José M Jerez | 5 | 7 | 0.82 |