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Benchmark Dataset and Deep Model for Monocular Camera Calibration from Single Highway Images.

Wentao Zhang¹, Wei Jia¹, Wei Li¹

¹School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

This summary is machine-generated.

This study introduces DeepCalib, a novel deep learning network for efficient single-image camera auto-calibration in traffic surveillance. It overcomes data scarcity and improves multi-view adaptability, achieving 89.6% precision.

Keywords:

deep learning highway scenes multi-view surveillance single-image calibration synthetic dataset

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Area of Science:

Computer Vision
Artificial Intelligence
Traffic Surveillance Systems

Background:

Single-image camera auto-calibration is crucial for traffic perception efficiency.
Existing methods struggle with limited real-world data and multi-view scenario adaptability.

Purpose of the Study:

To present a systematic solution framework for single-image camera auto-calibration.
To address the challenges of data scarcity and poor adaptability in multi-view traffic surveillance.

Main Methods:

Constructed a large-scale synthetic dataset (336,000 frames) using CARLA 0.9.15 with diverse highway scenarios.
Developed DeepCalib, a deep calibration network utilizing triplet attention for vanishing point localization and camera pose estimation.
Implemented a progressive learning paradigm: pre-training on synthetic data followed by fine-tuning on real-world data.

Main Results:

DeepCalib achieved an average calibration precision of 89.6%.
The method demonstrated a processing speed of 10 frames per second.
Showcased robust adaptability to dynamic calibration tasks across various surveillance views.

Conclusions:

The proposed framework effectively enhances camera auto-calibration in traffic surveillance.
DeepCalib offers a significant improvement over conventional multi-stage algorithms in terms of speed and adaptability.
The synthetic dataset and progressive learning approach contribute to better real-world performance.