DeepPTZ: Deep Self-Calibration for PTZ Cameras

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Rotating and zooming cameras, also called PTZ (Pan-Tilt-Zoom) cameras, are widely used in modern surveillance systems. While their zooming ability allows acquiring detailed images of the scene, it also makes their calibration more challenging since any zooming action results in a modification of their intrinsic parameters. Therefore, such camera calibration has to be computed online; this process is called self-calibration. In this paper, given an image pair captured by a PTZ camera, we propose a deep learning based approach to automatically estimate the focal length and distortion parameters of both images as well as the rotation angles between them. The proposed approach relies on a dual-Siamese structure, imposing bidirectional constraints. The proposed network is trained on a large-scale dataset automatically generated from a set of panoramas. Empirically, we demonstrate that our proposed approach achieves competitive performance with respect to both deep learning based and traditional state-of-the art methods. Our code and model will be publicly available at https://github.com/ChaoningZhang/DeepPTZ.
Publisher
Winter Conference on Applications of Computer Vision
Issue Date
2020-03
Language
English
Citation

IEEE Winter Conference on Applications of Computer Vision, WACV 2020, pp.1030 - 1038

ISSN
2472-6737
DOI
10.1109/WACV45572.2020.9093629
URI
http://hdl.handle.net/10203/278750
Appears in Collection
EE-Conference Papers(학술회의논문)
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