Towards feature-based situation assessment for airport apron video surveillance

Abstract

We present a feature-based surveillance pipeline which, in contrast to traditional image-based methods, allows to learn a detailed description of the observed background as well as of foreground objects. The pipeline consists of motion segmentation of feature trajectories and subsequent tracking-by- recognition with updates. Furthermore, 3D object representations are learned in order to extract the 3D object pose of a later object recognition. Finally, we show how such sufficiently reliable information is inputted into a reasoning system comparing actual and nominal condition of an airport apron. By this, automatic situation assessment becomes possible in a manageable and reliable way. © 2012 Springer-Verlag.