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Open AccessArticle

Camera Coverage Estimation Based on Multistage Grid Subdivision

by Meizhen Wang 1,2,3, Xuejun Liu 1,2,3,*, Yanan Zhang 1,2,3 and Ziran Wang 4
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Nanjing Normal University Taizhou College, Taizhou 225300, China
Author to whom correspondence should be addressed.
Academic Editors: Chi-Hua Chen, Kuen-Rong Lo and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(4), 110;
Received: 15 December 2016 / Revised: 27 March 2017 / Accepted: 31 March 2017 / Published: 5 April 2017
(This article belongs to the Special Issue Applications of Internet of Things)
Visual coverage is one of the most important quality indexes for depicting the usability of an individual camera or camera network. It is the basis for camera network deployment, placement, coverage-enhancement, planning, etc. Precision and efficiency are critical influences on applications, especially those involving several cameras. This paper proposes a new method to efficiently estimate superior camera coverage. First, the geographic area that is covered by the camera and its minimum bounding rectangle (MBR) without considering obstacles is computed using the camera parameters. Second, the MBR is divided into grids using the initial grid size. The status of the four corners of each grid is estimated by a line of sight (LOS) algorithm. If the camera, considering obstacles, covers a corner, the status is represented by 1, otherwise by 0. Consequently, the status of a grid can be represented by a code that is a combination of 0s or 1s. If the code is not homogeneous (not four 0s or four 1s), the grid will be divided into four sub-grids until the sub-grids are divided into a specific maximum level or their codes are homogeneous. Finally, after performing the process above, total camera coverage is estimated according to the size and status of all grids. Experimental results illustrate that the proposed method’s accuracy is determined by the method that divided the coverage area into the smallest grids at the maximum level, while its efficacy is closer to the method that divided the coverage area into the initial grids. It considers both efficiency and accuracy. The initial grid size and maximum level are two critical influences on the proposed method, which can be determined by weighing efficiency and accuracy. View Full-Text
Keywords: camera coverage estimation; multistage grid subdivision; line of sight; viewshed analysis; obstacle camera coverage estimation; multistage grid subdivision; line of sight; viewshed analysis; obstacle
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Wang, M.; Liu, X.; Zhang, Y.; Wang, Z. Camera Coverage Estimation Based on Multistage Grid Subdivision. ISPRS Int. J. Geo-Inf. 2017, 6, 110.

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