A siamese network-based approach for vehicle pose estimation

Haoyi Zhao; Bo Tao; Licheng Huang; Baojia Chen

doi:10.3389/fbioe.2022.948726

A siamese network-based approach for vehicle pose estimation

Front Bioeng Biotechnol. 2022 Sep 2:10:948726. doi: 10.3389/fbioe.2022.948726. eCollection 2022.

Authors

Haoyi Zhao^{1

2}, Bo Tao^{1

3}, Licheng Huang^{4

5}, Baojia Chen⁶

Affiliations

¹ Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.
² Wisdri Utility Tunnel Designing Institute, Wuhan, China.
³ Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China.
⁴ Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, China.
⁵ Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China.
⁶ Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, China.

Abstract

We propose a deep learning-based vehicle pose estimation method based on a monocular camera called FPN PoseEstimateNet. The FPN PoseEstimateNet consists of a feature extractor and a pose calculate network. The feature extractor is based on Siamese network and a feature pyramid network (FPN) is adopted to deal with feature scales. Through the feature extractor, a correlation matrix between the input images is obtained for feature matching. With the time interval as the label, the feature extractor can be trained independently of the pose calculate network. On the basis of the correlation matrix and the standard matrix, the vehicle pose changes can be predicted by the pose calculate network. Results show that the network runs at a speed of 6 FPS, and the parameter size is 101.6 M. In different sequences, the angle error is within 8.26° and the maximum translation error is within 31.55 m.

Keywords: contrast learning; correlation matrix; feature pyramid network; pose estimation; siamese network.