Name
Title | ||
|---|---|---|
An Efficient Image Stitching Method For Heterogeneous Car Videos Based On Bounding Boxes Of Features |
Abstract | ||
|---|---|---|
Heterogeneous car video recorders can capture scene information with different modalities including viewing angles, resolutions, and lens sensors. Traditional methods cannot accurately perform image stitching on the images captured by heterogeneous cameras. This paper presents an efficient method to stitch heterogeneous images by allowing a driver to view an ultra-wide angle without blind spots. It extracts bounding boxes of brake lights and license plate numbers as feature points to be matched. A homography matrix is computed to stitch the heterogeneous video images. Experimental results show that our proposed method can stitch images accurately and efficiently, which is superior to the existing methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1142/S0218001417550084 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Video stitching, heterogeneous recorder, ultra-wide-angle road scene, big view, car video recorder | Computer vision,Brake,Image stitching,Video stitching,Computer graphics (images),Blind spot,Homography,Artificial intelligence,Mathematics,Bounding overwatch | Journal |
Volume | Issue | ISSN |
31 | 5 | 0218-0014 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chun-Ming Tsai | 1 | 1 | 3.06 |
| Frank Y. Shih | 2 | 1103 | 89.56 |