Visual Expansion and Real‐Time Calibration for Pan‐Tilt‐Zoom Cameras Assisted by Panoramic Models

ABSTRACT Pan‐tilt‐zoom (PTZ) cameras, which dynamically adjust their field of view (FOV), are pervasive in large‐scale scenes, such as train stations, squares, and airports. In real scenarios, PTZ cameras are required to quickly judge their directions using contextual clues from the surrounding envi...

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Published inComputer animation and virtual worlds Vol. 36; no. 1
Main Authors Cai, Liangliang, Zhou, Zhong
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.01.2025
Wiley Subscription Services, Inc
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Abstract ABSTRACT Pan‐tilt‐zoom (PTZ) cameras, which dynamically adjust their field of view (FOV), are pervasive in large‐scale scenes, such as train stations, squares, and airports. In real scenarios, PTZ cameras are required to quickly judge their directions using contextual clues from the surrounding environment. To achieve this goal, some research projects camera videos into three‐dimensional (3D) models or panoramas and allows operators to establish spatial relationships. However, these works face several challenges in terms of real‐time processing, localization accuracy, and realistic reference. To address this problem, a visual expansion and real‐time calibration for PTZ cameras assisted by panoramic models is proposed. The calibration method consists of three parts: Providing a real environment background by building a panoramic model, meeting the needs of real‐time processing by establishing a PTZ camera motion estimation model and achieving high‐precision alignment between PTZ images and panoramic models using only two feature point pairs. Our methods were validated using both the public and our Scene dataset. The experimental results indicate that our method outperforms other state‐of‐the‐art methods in terms of real‐time processing, accuracy, and robustness. We propose a visual expansion and real‐time calibration for PTZ cameras assisted by panoramic models. The calibration method consists of three parts: Providing a real environment background by building a panoramic model, establishing a PTZ camera motion estimation model for meeting the needs of real‐time processing, and achieving high‐precision alignment between PTZ images and panoramic models using only two feature point pairs.
AbstractList ABSTRACT Pan‐tilt‐zoom (PTZ) cameras, which dynamically adjust their field of view (FOV), are pervasive in large‐scale scenes, such as train stations, squares, and airports. In real scenarios, PTZ cameras are required to quickly judge their directions using contextual clues from the surrounding environment. To achieve this goal, some research projects camera videos into three‐dimensional (3D) models or panoramas and allows operators to establish spatial relationships. However, these works face several challenges in terms of real‐time processing, localization accuracy, and realistic reference. To address this problem, a visual expansion and real‐time calibration for PTZ cameras assisted by panoramic models is proposed. The calibration method consists of three parts: Providing a real environment background by building a panoramic model, meeting the needs of real‐time processing by establishing a PTZ camera motion estimation model and achieving high‐precision alignment between PTZ images and panoramic models using only two feature point pairs. Our methods were validated using both the public and our Scene dataset. The experimental results indicate that our method outperforms other state‐of‐the‐art methods in terms of real‐time processing, accuracy, and robustness. We propose a visual expansion and real‐time calibration for PTZ cameras assisted by panoramic models. The calibration method consists of three parts: Providing a real environment background by building a panoramic model, establishing a PTZ camera motion estimation model for meeting the needs of real‐time processing, and achieving high‐precision alignment between PTZ images and panoramic models using only two feature point pairs.
Pan‐tilt‐zoom (PTZ) cameras, which dynamically adjust their field of view (FOV), are pervasive in large‐scale scenes, such as train stations, squares, and airports. In real scenarios, PTZ cameras are required to quickly judge their directions using contextual clues from the surrounding environment. To achieve this goal, some research projects camera videos into three‐dimensional (3D) models or panoramas and allows operators to establish spatial relationships. However, these works face several challenges in terms of real‐time processing, localization accuracy, and realistic reference. To address this problem, a visual expansion and real‐time calibration for PTZ cameras assisted by panoramic models is proposed. The calibration method consists of three parts: Providing a real environment background by building a panoramic model, meeting the needs of real‐time processing by establishing a PTZ camera motion estimation model and achieving high‐precision alignment between PTZ images and panoramic models using only two feature point pairs. Our methods were validated using both the public and our Scene dataset. The experimental results indicate that our method outperforms other state‐of‐the‐art methods in terms of real‐time processing, accuracy, and robustness.
Author Cai, Liangliang
Zhou, Zhong
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Snippet ABSTRACT Pan‐tilt‐zoom (PTZ) cameras, which dynamically adjust their field of view (FOV), are pervasive in large‐scale scenes, such as train stations, squares,...
Pan‐tilt‐zoom (PTZ) cameras, which dynamically adjust their field of view (FOV), are pervasive in large‐scale scenes, such as train stations, squares, and...
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crossref
wiley
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Publisher
SubjectTerms Accuracy
augmented virtual environment
Calibration
camera calibration
Cameras
Field of view
key‐ray collection
Motion simulation
panorama
Point pairs
PTZ camera
Railway stations
Research projects
Title Visual Expansion and Real‐Time Calibration for Pan‐Tilt‐Zoom Cameras Assisted by Panoramic Models
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fcav.70015
https://www.proquest.com/docview/3171219276
Volume 36
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