A spatial adaptive differential evolution approach for optimal surveillance camera deployment considering spatial importance

Video surveillance cameras are a critical component of urban security systems, yet traditional deployment strategies often result in blind spots, redundant coverage, and suboptimal resource utilization, particularly in complex three-dimensional environments. To address these issues, this study introduces the spatial adaptive differential evolution algorithm (SA-DEA), a novel approach to optimize surveillance camera placement based on multi-constraint spatial visibility analysis of high-precision 3D geospatial models. This method ensures comprehensive coverage, maximizes resource efficiency, and incorporates spatial significance. The key innovations of the SA-DEA include: (1) joint optimization of camera positions and orientations with adaptive parameter control to refine the search process and enhance convergence; (2) multi-type mutation mechanisms and harmony search crossover to improve adaptability to complex geographical environments; and (3) a multi-attribute weighted aggregation fitness function that integrates coverage, redundancy, and spatial importance for a comprehensive evaluation. Experimental results demonstrate that the proposed approach outperforms existing methods in terms of area coverage, reduced redundancy, and enhanced surveillance of critical regions. In complex 3D environments, the SA-DEA exhibits robust global search capabilities and significant practical value, offering new insights into spatial optimization for urban intelligent surveillance systems.