Rapid survey method for large-scale outdoor surveillance cameras using binary space partitioning

Video surveillance systems have been widely used in public security and urban management. Following the increasing demand for video surveillance in various countries worldwide, the number of outdoor surveillance cameras is rising. Their unified management and layout optimization have become a great challenge in the construction of video surveillance. Although there is a large number of outdoor surveillance cameras, there exists a lack of spatial location information, leading to difficulties regarding their quantification, space management, and construction status analysis. Hence, a method for estimating the location and orientation of outdoor surveillance cameras based on spatial segmentation and the use of a rapid survey device was proposed. Using the position information obtained from the movement of a designed marker and surveillance images simultaneously captured by each camera, we constructed an initial camera space and segmentation plane using binary space partitioning. By recursive segmentation, the real location of each camera is gradually approximated, and the azimuth angle of the camera orientation is calculated, achieving the rapid estimation of the spatial location and orientation of all surveillance cameras. A city area spanning 2.8 km2 was selected as an experimental area to verify the method proposed here, yielding an average error of 1.78 m between the estimated and actual locations of the cameras and a maximum error of 3.87 m. The average offset between the estimated and actual orientation was 3.13 degrees, with a maximum offset of 6 degrees. Thus, the target of the surveillance camera's estimated deviation is still in the visible range of the operator. The proposed method represents a new way for the rapid investigation of outdoor surveillance cameras and provides a basis for their unified management and layout optimization.