| Abstrakt: |
The problem of optimal camera placement (OCP) is the objective today, to identify the configurations of camera networks that optimize the area of interest under a set of constraints. In this article, we presented a novel technique based on new binary Particle Swarm Optimization (NBPSO) stimulated probability is anticipated to solve the camera placement problem. Ensuring correct visual coverage area of the monitoring hole with a minimum number of cameras is required. The illustration coverage is defined by practical and consistent assumptions attractive into description camera characteristics. After that, we proposed evolutionary of algorithms based on NBPSO and particle swarm optimization (PSO) are adapted to answer this optimal coverage based camera placement problem. These techniques are introduced in the context of processing constrained optimizations. We consider the case of presence of obstacles which could affect the placement of the cameras. Indeed, there could be a set of points not observed because of the imposed geometry of the obstacles, which occlude the fields of view entering their areas. The performance of NBPSO method stimulated is compared by the techniques many (e.g., genetic algorithms-based (GA), binary Particle Swarm Optimization BPSO, SA…). The results of the simulation were developed for the 2D scenarios which showed the fragrances of the proposed approach. Indeed, for a major element case, NBPS stimulated gives results good the ones obtained by adapting all variants. |
Full text from SpringerLink