Good feature for framing: Saliency-based Gaussian Mixture

In this paper, we present a new automatic camera control to achieve a relevant information framing. This camera control will be performed using a visual servoing framework in order to reach a salient area in a plane and in space. The relevant framing will be modelled by maximizing the saliency-based Gaussian Mixture Model (GMM) feature in the image. Furthermore, in order to achieve a realistic automatic camera control, we add an obstacles avoidance constraint and we ensure a relevant orientation during the motion. We validate our contribution in different synthetic 2D and 3D environments. Finally, we test our approach on a dense 3D points cloud model and in a real environment with a robot.