A differential evolution approach for coverage optimization of visual sensor networks with parallel occlusion detection

摘要

This paper investigates the coverage optimization of a visual sensor network for monitoring three-dimensional (3-D) environment or objects. Different from existing works, we improve the system performance from two aspects: (1) a parallel visual occlusion detection algorithm is implemented with Graphic Processing Units (GPUs) in order to increase the computing efficiency and a further improved parallel visual occlusion detection algorithm is introduced to reduce the complexity of the problem; (2) Differential Evolution (DE) is first applied to optimize deployment configurations of visual sensor networks. Comparative evaluation results demonstrate the superior performance of the proposed approach.