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 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.