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This study processes an autonomous indoor drone photographer that searches for and selects a heuristic optimal viewpoint to obtain a well-composed photograph of a group of subjects. The subjects on the drone&#x2019;s camera screen are represented by a Gaussian mixture model. When there are four or more subjects, they are represented by a Gaussian mixture model with clustering by variational Bayes. The Kullback&#x2013;Leibler divergence is evaluated between the Gaussian mixture model and a user-defined reference composition, and it is defined as the composition evaluation value. The reference composition is pre-set by the user based on the basic composition rules, such as the three-section method. The drone searches for a viewpoint in a 3D space to optimize the composition evaluation value using particle swarm optimization (PSO). A Gaussian process is used to facilitate the PSO search. This enables the drone to significantly reduce the search time and successfully capture a photograph with a well-balanced composition.

Optimal Viewpoint Selection by Indoor Drone Using PSO and Gaussian Process With Photographic Composition Based on KL Divergence