Toward Planet-Wide Traffic Camera Calibration

Despite the widespread deployment of outdoor cameras, their potential for automated analysis remains largely untapped due, in part, to calibration challenges. The absence of precise camera calibration data, including intrinsic and extrinsic parameters, hinders accurate real-world distance measuremen...

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Bibliographic Details
Main Authors: Vuong, Khiem, Tamburo, Robert, Narasimhan, Srinivasa G.
Format: Conference Proceeding
Language:English
Subjects:
3D computer vision
Algorithms
Applications
Autonomous Driving
Calibration
Cameras
Computer vision
Distance measurement
Location awareness
Social good
Solid modeling
Three-dimensional displays
Online Access:Request full text
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Description
Summary:Despite the widespread deployment of outdoor cameras, their potential for automated analysis remains largely untapped due, in part, to calibration challenges. The absence of precise camera calibration data, including intrinsic and extrinsic parameters, hinders accurate real-world distance measurements from captured videos. To address this, we present a scalable framework that utilizes street-level imagery to reconstruct a metric 3D model, facilitating precise calibration of in-the-wild traffic cameras. Notably, our framework achieves 3D scene reconstruction and accurate localization of over 100 global traffic cameras and is scalable to any camera with sufficient street-level imagery. For evaluation, we introduce a dataset of 20 fully calibrated traffic cameras, demonstrating our method's significant enhancements over existing automatic calibration techniques. Furthermore, we highlight our approach's utility in traffic analysis by extracting insights via 3D vehicle reconstruction and speed measurement, thereby opening up the potential of using outdoor cameras for automated analysis. Code and dataset will be available on the project website.
ISSN:2642-9381
DOI:10.1109/WACV57701.2024.00836