Darknet for 4-point homography estimation

Teliang Wang; Qian Yin; Wei An; Miao Li; Zaiping Lin

doi:10.1117/12.2674304

28 April 2023 Darknet for 4-point homography estimation

Teliang Wang, Qian Yin, Wei An, Miao Li, Zaiping Lin

Author Affiliations +

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126261O (2023) https://doi.org/10.1117/12.2674304
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

Homography estimation is often an indispensable step in computer vision tasks that require multi-frame time-domain information. However, when we estimate the traditional homography matrix, the rotational and translational terms are often difficult to balance. In this paper, based on the 4-point homography parameter matrix, we reproduce the Synthetic COCO dataset (S-COCO) and the Photometrically Distorted Synthetic COCO dataset (PDS-COCO). Then, we use the Darknet in YOLOv3 as the backbone to design a deep network for 4-point homography estimation. Experiments show that compared with existing main one-stop methods, our proposed deep learning network achieves the best performance on the S-COCO dataset and excellent performance on the PDS-COCO dataset.

Citation Download Citation

Teliang Wang, Qian Yin, Wei An, Miao Li, and Zaiping Lin "Darknet for 4-point homography estimation", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126261O (28 April 2023); https://doi.org/10.1117/12.2674304

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE