Demo Data from SIGIR 2026 / reviewers in the wild / expert
Zeyu Shang
dblp:191/4646
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 2
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
camera calibration |
0.2 | 1 | 2016 | Camera Calibration from Periodic Motion of a Pedestrian · CVPR 2016 |
Computer vision › 3D vision › camera calibration
intrinsic and extrinsic parameter estimation |
0.1 | 1 | 2016 | Camera Calibration from Periodic Motion of a Pedestrian · CVPR 2016 |
Methods — techniques the papers use, named apart from their topics
projective geometry · 0.2harmonic conjugate · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Radial Lens Distortion Correction Using Convolutional Neural Networks Trained with Synthesized Images
Jiangpeng Rong, Shiyao Huang, Zeyu Shang, Xianghua Ying |
ACCV (3) | 3 |
| 2016 | Camera Calibration from Periodic Motion of a PedestrianabstractCamera 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. Shiyao Huang, Xianghua Ying, Jiangpeng Rong, Zeyu Shang, Hongbin Zha |
CVPR | 4 |