Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Zeyu Shang

dblp:191/4646 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
camera calibration
0.212016
Camera Calibration from Periodic Motion of a Pedestrian · CVPR 2016
Computer vision › 3D vision › camera calibration
intrinsic and extrinsic parameter estimation
0.112016
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
YearPublicationVenuePosition
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 Pedestrian
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.
Shiyao Huang, Xianghua Ying, Jiangpeng Rong, Zeyu Shang, Hongbin Zha
CVPR4