Name
Abstract | ||
|---|---|---|
Sleep mode detection is one of the significant features of power management and green computing. However, for a television or a smart TV, it is difficult to detect a deactivation event because the user can use these devices without input from an input device. We propose a robust method to detect deactivation events based on a vision approach involving face detection and motion detection for a smart TV. Experiments are performed on a large dataset. The proposed approach significantly reduces false detections of faces and complement missed humans by means of motion detection. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICCE.2013.6486823 | ICCE |
Keywords | Field | DocType |
face recognition,image motion analysis,television,deactivation events detection,face detection,green computing,motion detection,power management feature,robust method,smart tv,vision approach,vision-based sleep mode detection | Computer vision,Power management,Facial recognition system,Green computing,Motion detection,Object-class detection,Computer science,Artificial intelligence,Face detection,Sleep mode,Input device | Conference |
ISSN | ISBN | Citations |
2158-3994 | 978-1-4673-1361-2 | 1 |
PageRank | References | Authors |
0.35 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yeong Nam Chae | 1 | 6 | 3.55 |
| Suwon Lee | 2 | 23 | 6.28 |
| ByungOk Han | 3 | 14 | 2.03 |
| Yang, H.S. | 4 | 1 | 0.35 |