Active object tracking, for example, in surveillance tasks, becomes more and more important these days. Besides the tracking algorithms themselves methodologies have to be developed for reasonable active control of the degrees of freedom of all involved cameras. We present an information theoretic approach that allows the optimal selection of the focal lengths of two cameras during active 3D object tracking. The selection is based on the uncertainty in the 3D estimation. This allows us to resolve the trade-off between small and large focal length: in the former case, the chance is increased to keep the object in the field of view of the cameras. In the latter one, 3D estimation becomes more reliable. Also, more details are provided, for example for recognizing the objects. Beyond a rigorous mathematical framework we present real-time experiments demonstrating that we gain an improvement in 3D trajectory estimation by up to 42% in comparison with tracking using a fixed focal length.

In active visual 3D object tracking, one goal is to control the pan and tilt axes of the involved cameras to keep the tracked object in the centers of the fields of view. We present a novel method, based on an information theoretic measure, that manages this task. The main advantage of the proposed approach is that there is no need for an explicit formulation of a camera controller, such as a PID-controller or something similar. For the case of Kalman filter based tracking, we demonstrate the practicability and evaluate the accuracy of the proposed method in simulations as well as in real-time tracking experiments.

We consider the problem of robust localization of faces and some of their facial features. The task arises, e.g., in the medical field of visual analysis of facial paresis. We detect faces and facial features by means of appropriate DCT coefficients that we obtain by neatly using the coding capabilities of a JPEG hardware compressor. Beside an anthropometric localization approach we focus on how spatial coupling of the facial features can be used to improve robustness of the localization. Because the presented approach is embedded in a completely probabilistic framework, it is not restricted to facial features, it can be generalized to multipart objects of any kind. Therefore the notion of a "coupled structure" is introduced. Finally, the approach is applied to the problem of localizing facial features in DCT-coded images and results from our experiments are shown.