Active multi-camera object recognition in presence of occlusion

This paper is concerned with the problem of appearance-based active multi-sensor object recognition/pose estimation in the presence of structured noise. It is assumed that multiple cameras acquire images from an object belonging to a set of known objects. An algorithm is proposed for optimal sequential positioning of the cameras in order to estimate the class and pose of the object from sensory observations. The principle component analysis is used to produce the observation vector from the acquired images. Object occlusion and sensor noise have been explicitly incorporated into the recognition process using a probabilistic approach. A recursive Bayesian state estimation problem is formulated that employs the mutual information in order to determine the best next camera positions based on the available information. Experiments with a two-camera system demonstrate that the proposed method is highly effective in object recognition/pose estimation in the presence of occlusion.