Single-frame Image Motion Parallax Key Point Estimation Combined with Self-supervised Learning

Authors

Zhihao Huo, 1. School of Electric Engineering, Southwest Jiaotong University, Chengdu 611576, China;;
Weidong Jin, 1. School of Electric Engineering, Southwest Jiaotong University, Chengdu 611576, China;;2. China-ASEAN International Joint Laboratory of Integrated Transport, Nanning University, Guangxi 530200, China;
Tang Peng, 1. School of Electric Engineering, Southwest Jiaotong University, Chengdu 611576, China;;

Abstract

Abstract: The motion parallax key point FOE (Focus of Expansion) is an important parameter of railway catenary video inspection. The current method of calculating FOE requires multi-frame image matching estimation, which has high time complexity. Aiming at the single-frame image FOE estimation, a single-frame image FOE estimation algorithm fused with self-supervised learning is proposed. A full convolutional network F-VGG(Fully-Visual Geometry Group) is built as the FOE predictor, and the training label of the sample data is automatically generated through the fusion agent task, which realizes the end-to-end single-frame image FOE estimation. The experimental results show that the method has an average increase of 13.45% in FOE prediction accuracy, and an increase of 56.27% in detection speed, which is suitable for real-time applications.

Recommended Citation

Huo, Zhihao; Jin, Weidong; and Peng, Tang (2021) "Single-frame Image Motion Parallax Key Point Estimation Combined with Self-supervised Learning," Journal of System Simulation: Vol. 33: Iss. 11, Article 24.
DOI: 10.16182/j.issn1004731x.joss.21-FZ0708
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss11/24

First Page

2753

Revised Date

2021-08-18

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-FZ0708

Last Page

2759

CLC

TP391.4

Recommended Citation

Huo Zhihao, Jin Weidong, Tang Peng. Single-frame Image Motion Parallax Key Point Estimation Combined with Self-supervised Learning[J]. Journal of System Simulation, 2021, 33(11): 2753-2759.

Corresponding Author

Weidong Jin,

DOI

10.16182/j.issn1004731x.joss.21-FZ0708