Integrated Viewpoint Fusion and Viewpoint Selection for Optimal Object Recognition

In the past decades, most object recognition systems were based on passive approaches. But in the last few years a lot of research was done in the field of active object recognition, that is selectively moving a sensor/camera around a considered object in order to acquire as much information about it as possible. In this paper we present an active object recognition approach that solves the problem of choosing optimal views (viewpoint selection) and iteratively fuses the gained information for an optimal 3D object recognition (viewpoint fusion) in an integrated manner. Therefore, we apply a method for the fusion of multiple views with respect to the knowledge about the assumed camera movement between them. For viewpoint selection we formally define the choice of additional views as an optimization problem. We show how to use reinforcement learning for this purpose and perform a training without user interaction. In this context we focus on the modeling of continuous states, continuous, one-dimensional actions and supporting rewards for an optimized recognition of real objects. The experimental results show that our combined viewpoint selection and viewpoint fusion approach is able to significantly improve the recognition rates compared to passive object recognition with randomly chosen views.