A genetic algorithm-based search method of camera deployment conditions for cooperative observation by AI

Abstract

The data obtained from various sensors is utilized for spatial analysis utilizing AI. To maximize the effectiveness of AI-based spatial analysis, it is necessary to estimate in advance factors such as the placement, installation angles, and quantity of multiple sensors. To make this possible, a search method for camera placement conditions based on iterative deepening depth-first search utilizing an evaluation function based on object detection results was constructed. However, there is a challenge of falling into local optima due to the limited search space. Therefore, in this paper, a search method for camera placement conditions based on genetic algorithms is proposed. Specifically, the solution obtained by the conventional method is used as the initial solution, and selection, crossover, and mutation are iteratively applied to explore camera placement conditions. This allows for searching broader search compared to conventional methods and achieves a method less prone to local optimal solution. The effectiveness of the proposed method is confirmed through experiments.