Name
Abstract | ||
|---|---|---|
The automatic calibration of the intrinsic camera parameters such as the focal length and the camera orientation is an important pre-requisite for many computer vision algorithms in video surveillance. Despite its importance only a few number of methods show their applicability in embedded systems. This paper shows new results of previous work done in camera self-calibration on images of the York Urban data-set. These 102 images show typical urban scenes one might expect in practice. The evaluation shows that in 52 of 102 images the proposed method achieves less than 5% relative error in the focal length at a mean computation time per image of 14.45 s on a standard PC. We believe that these results show a fair balance between accuracy and computational performance and encourage an embedded implementation on a smart camera. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICCVW.2009.5457614 | Computer Vision Workshops |
Keywords | Field | DocType |
calibration,computer vision,video cameras,video signal processing,video surveillance,automatic calibration,camera orientation,camera self-calibration,embedded systems,focal length,intrinsic camera parameters,mean computation time,self-calibration method,smart camera,smart video cameras,accuracy,image segmentation,robustness,estimation,lenses | Computer vision,Computer science,Camera auto-calibration,Smart camera,Image segmentation,Focal length,Robustness (computer science),Artificial intelligence,Calibration,Approximation error,Computation | Conference |
Volume | Issue | ISBN |
2009 | 1 | 978-1-4244-4441-0 |
Citations | PageRank | References |
1 | 0.39 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nebehay, G. | 1 | 1 | 0.39 |
| Roman P. Pflugfelder | 2 | 95 | 11.36 |