Composition Optimization of Moving Objects Using Recursive Gaussian Process for Photography Drone
Taisei Yokomatsu, Kosuke Sekiyama · 2023
In this study, we developed a process of heuristically searching and selecting the optimal viewpoint for capturing a good composition of a group of moving subjects with an autonomous indoor drone camera. The subjects on the drone's camera screen are represented using a Gaussian mixture model. The Kullback–Leibler divergence between the Gaussian mixture model and a user-defined reference composition is evaluated and defined as the composition evaluation value. The drone searches for a viewpoint in a 3D space to optimize this value using particle swarm optimization. To facilitate the search, a recursive Gaussian process is employed to update the prediction of the observation result. Through the proposed method, a sufficient optimal viewpoint can be obtained, even for moving subjects.