Name
Abstract | ||
|---|---|---|
Camera calibration directly from image sequences of a pedestrian without using any calibration object is a really challenging task and should be well solved in computer vision, especially in visual surveillance. In this paper, we propose a novel camera calibration method based on recovering the three orthogonal vanishing points (TOVPs), just using an image sequence of a pedestrian walking in a straight line, without any assumption of scenes or motions, e.g., control points with known 3D coordinates, parallel or perpendicular lines, non-natural or pre-designed special human motions, as often necessary in previous methods. The traces of shoes of a pedestrian carry more rich and easily detectable metric information than all other body parts in the periodic motion of a pedestrian, but such information is usually overlooked by previous work. In this paper, we employ the images of the toes of the shoes on the ground plane to determine the vanishing point corresponding to the walking direction, and then utilize harmonic conjugate properties in projective geometry to recover the vanishing point corresponding to the perpendicular direction of the walking direction in the horizontal plane and the vanishing point corresponding to the vertical direction. After recovering all of the TOVPs, the intrinsic and extrinsic parameters of the camera can be determined. Experiments on various scenes and viewing angles prove the feasibility and accuracy of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CVPR.2016.330 | 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
Field | DocType | Volume |
Computer vision,Line (geometry),Perpendicular,Computer science,Vertical direction,Projective geometry,Camera auto-calibration,Camera resectioning,Artificial intelligence,Horizontal plane,Vanishing point | Conference | 2016 |
Issue | ISSN | Citations |
1 | 1063-6919 | 1 |
PageRank | References | Authors |
0.35 | 15 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiyao Huang | 1 | 4 | 0.73 |
| Xianghua Ying | 2 | 221 | 23.55 |
| Jiangpeng Rong | 3 | 4 | 1.07 |
| Zeyu Shang | 4 | 4 | 0.73 |
| Hongbin Zha | 5 | 2206 | 183.36 |