Deployment optimization for camera network coupling with geographic environment

Abstract

Surveillance cameras are widely deployed to monitor the geographical environment, traffic condition, public security, so on. Optimal deployment of surveillance cameras is an effective way to promote their validity and utilization. Because of the ignorance of geographic environment in major existing researches, a new optimization method which considers the constraining relationships between cameras and geographic environment is proposed. Firstly, coverage models for single camera and camera network which consider the occlusions in geographic environment are discussed. Secondly, the objective of optimization is put forward, which is to employ the minimum cameras to monitor the specific areas. Thirdly, to achieve the objective, the heuristic search algorithm is introduced. The deployable camera pose and location, and target area are discretized to promote the efficiency. Consequently, the deployment optimization problem is abstracted into a combinatorial optimization problem. Then optimization model including the graph structure, cost function and heuristic function for the algorithm is built to meet the requirement of the heuristic search algorithm, and its correctness is also strictly proofed. Finally, experiment in real city area with occlusions (such as building) validates the usability of our method. The sampling steps of camera pose and location and the split size of target area for discrete process are two important factors to influence the degree of accuracy and efficiency. The results obtained by our method can provide decision support for optimal camera deployment in real geographic environment, as well as provide reference for other environmental monitoring sensor deployment optimization tasks. ARTICLE HISTORY

Adaptive attention-based human machine interface system for teleoperation of industrial vehicle

“…In this study, M = 8 and the positions of each camera are illustrated in Appendix B . To select the optimal visual stimuli, camera coverage of a set of gaze fixation points v(s) is computed based on 13 . The model of a camera is given by C = (X c , Y c , Z c , P, T, ccd w , ccd h , f), where (X c , Y c , Z c ) is the position of the camera’s optical center, (P,T) is the yaw and pitch angles, and (ccd w , ccd h , f) is width, height, and focal length of the imaging plane.…”

Reflections and speculations on the progress in Geographic Information Systems (GIS): a geographic perspective

“…Therefore, the introduction of video technology can contribute to both data acquisition and vivid expression (e.g. Yang et al 2001;Wang et al 2018b). More importantly, compared with static images, videos contain abundant geographic process-related information beyond spatial and attribute information.…”

Multi-Objective Optimal Deployment of Road Traffic Monitoring Cameras: A Case Study in Wujiang, China

“…The optimal deployment for traffic cameras under different conditions was finally obtained. A new optimization method that considers the constraining relationships between the traffic cameras and the geographic environment was proposed by Wang et al [18]. The optimal objective was to minimize the number of the traffic camera settings, so an optimization model that included the graph structure, cost function, and heuristic function for the algorithm was developed.…”