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Type: Conference paper
Title: Large-scale camera network topology estimation by lighting variation
Author: Zhu, M.
Dick, A.
van den Hengel, A.
Citation: Lecture Notes in Artificial Intelligence, 2017 / Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (ed./s), vol.10617 LNCS, pp.455-467
Publisher: Springer
Issue Date: 2017
Series/Report no.: Lecture Notes in Computer Science; 10617
ISBN: 9783319703527
ISSN: 0302-9743
Conference Name: 18th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2017) (18 Sep 2017 - 21 Sep 2017 : Antwerp, Belgium)
Editor: Blanc-Talon, J.
Penne, R.
Philips, W.
Popescu, D.
Scheunders, P.
Statement of
Michael Zhu, Anthony Dick, Anton van den Hengel
Abstract: This paper proposes a scalable and robust algorithm to find connections between cameras in a large surveillance network, based solely on lighting variation. We show how to detect regions that are affected by lighting changes within each camera view, with limited data. Then, we establish the light-overlap connections and show that our algorithm can scale to hundreds of camera while maintaining high accuracy. We demonstrate our method on a campus network of 100 real cameras and 500 simulated cameras, and evaluate its accuracy and scalability.
Keywords: Large-scale intelligent video surveillance; topology estimation; light-overlap; lighting variation detection; segmentation
Rights: ┬ęSpringer International Publishing AG 2017
DOI: 10.1007/978-3-319-70353-4_39
Appears in Collections:Aurora harvest 8
Australian Institute for Machine Learning publications
Computer Science publications

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