Information theoretic focal length selection for real-time active 3D object tracking

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.