Art gallery theorems and algorithms
Art gallery theorems and algorithms
Object tracking in the presence of occlusions via a camera network
Proceedings of the 6th international conference on Information processing in sensor networks
An immune based two-phase approach for the multiple-type surveillance camera location problem
Expert Systems with Applications: An International Journal
Can you see me now? sensor positioning for automated and persistent surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Camera placement using particle swarm optimization in visual surveillance applications
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Parallel particle swarm optimization (PPSO) on the coverage problem in pursuit-evasion games
Proceedings of the 2012 Symposium on High Performance Computing
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This paper studies how to improve the field of view (FOV) coverage of a camera network. We focus on a special but practical scenario where the cameras are randomly scattered in a wide area and each camera may adjust its orientation but cannot move in any direction. We propose a particle swarm optimization (PSO) algorithm which can efficiently find an optimal orientation for each camera. By this optimization the total FOV coverage of the whole camera network is maximized. This new method can also deal with additional constraints, such as a variable region of interest (ROI) and possible occlusions in the ROI. The experiments showed that the proposed method has a much better performance and a wider application scope. It can be effectively applied in the design of any practical camera network.