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.
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Dragon, R., Fenzi, M., Siberski, W., Rosenhahn, B., Ostermann, J. (2012). Towards Feature-Based Situation Assessment for Airport Apron Video Surveillance. In: Dellaert, F., Frahm, JM., Pollefeys, M., Leal-Taixé, L., Rosenhahn, B. (eds) Outdoor and Large-Scale Real-World Scene Analysis. Lecture Notes in Computer Science, vol 7474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34091-8_5
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DOI: https://doi.org/10.1007/978-3-642-34091-8_5
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