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Aug 30, 2023
Real-Time End-to-End Vehicle and Landmark Localization Based on Semi-Supervised Learning
Authors: Nengfei Xiao [email protected], Zhongxia Xiong [email protected], Yalong Ma [email protected], and Xinkai Wu [email protected]Author Affiliations
Publication: CICTP 2023
ABSTRACT
Detection of vehicles along with their landmarks is important for many subsequent topics, such as monocular 3D detection, vehicle tracking, and vehicle re-identification. However, due to lack of fully annotated datasets, currently, most research addresses this problem based on time-consuming two-stage schemes, i.e., firstly, detecting the bounding boxes of vehicles, then, cropping the vehicles, and regressing their landmarks based on these snapshots. In this paper, we develop a semi-supervised learning mechanism, which utilizes partially annotated data to train an end-to-end network that can simultaneously detect vehicles and their landmarks. In addition, our model is scalable and also provides a light-weight version of the proposed detection network which can run at a real-time speed of over 20 FPS on an edge device. Experimental reports indicate that this approach achieves satisfactory performance and is efficient for real-world application.
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Published online: Aug 30, 2023
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ASCE Technical Topics:
- Automation and robotics
- Big data
- Computer vision and image processing
- Detection methods
- Engineering fundamentals
- Highway transportation
- Infrastructure
- Intelligent transportation systems
- Measurement (by type)
- Methodology (by type)
- Research methods (by type)
- Sensors and sensing
- Systems engineering
- Tracking
- Transportation engineering
- Transportation management
- Vehicles
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1School of Transportation Science and Engineering, Beihang Univ., Beijing, China. Email: [email protected]
2School of Transportation Science and Engineering, Beihang Univ., Beijing, China. Email: [email protected]
3School of Transportation Science and Engineering, Beihang Univ., Beijing, China. Email: [email protected]
4School of Transportation Science and Engineering, Beihang Univ., Beijing, China. Email: [email protected]
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