Locating and Optimizing Coverage of CCTV Cameras to Support Better Monitoring Using S-ROPE Algorithm and 3D Visualization

Modern surveillance systems based on CCTV cameras is an essential element for protecting the environment and social security. Camera network optimization and designing its architecture are among the issues of camera network studies. The purpose of this paper is to develop a geospatial solution to find configurations for CCTV cameras in such a way that creates the maximum possible visual coverage in an urban area.

In general, this research is performed in two steps. In the first step, the algorithm is used to locate cameras in two-dimensional space, and the resulting output is analyzed in the second step in a three-dimensional space and visually. The first step was performed using ArcGIS software and Python programming language, and the S-ROPE algorithm was used as a high-precision method for 2D camera deployment. After the modifications were made at the viewing and non-binary regions of the region, the location of the cameras was determined. In the second stage, the three-dimensional model of City Engine software was used to validate the output obtained using the S-ROPE algorithm. The evaluation of the applied method was performed on an urban study area.

With the S-ROPE algorithm, an automated location determination for cameras was taken so that the area of 1798.28 m² was covered by a total area of 1953.98 m² of study area, i.e. 92%. After a three-dimensional review, only two cameras were added to the total of cameras to cover 100%.

With the proposed method, the number of cameras used makes significant savings, and the most possible coverage is achieved. The only challenge is the process time for large areas, which, due to the non-urgent nature of the problem, does not create a dent in the proposed method.