@inproceedings{ce5cb74b06de444abe5ba656a89b44c8,
title = "Coarse-to-fine multi-camera network topology estimation",
abstract = "In multiple camera networks, the correlation of multiple cameras can provide us with a richer information than a single camera. In order to make full use of the association information between multiple cameras. We propose a novel approach to estimate a camera topology relationship in a multi-camera surveillance network, which is unsupervised and gradually refined from coarse to fine. First, an improved cross-correlation function is used to get a preliminary result, then a time constraint feature matching model is used to reduce the error caused by external environment and noise, which can increase the accuracy of our results. Finally, we test the proposed method on several different datasets, and its result indicates that our approach perform well on recovering the topology of the camera and can improve the accuracy on over camera tracking.",
keywords = "Camera topology, Cross-correlation, Feature matching, Multi-camera",
author = "Chang Xing and Sichen Bai and Yi Zhou and Zhong Zhou and Wei Wu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 18th Pacific-Rim Conference on Multimedia, PCM 2017 ; Conference date: 28-09-2017 Through 29-09-2017",
year = "2018",
doi = "10.1007/978-3-319-77383-4\_96",
language = "英语",
isbn = "9783319773827",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "981--990",
editor = "Bing Zeng and Hongliang Li and Qingming Huang and \{El Saddik\}, Abdulmotaleb and Shuqiang Jiang and Xiaopeng Fan",
booktitle = "Advances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers",
address = "德国",
}