Visual surveillance in maritime port facilities

In this work we propose a method for securing port facilities which uses a set of video cameras to automatically detect various vessel classes moving within buffer zones and off-limit areas. Vessels are detected by an edge-enhanced spatiotemporal optimal trade-off maximum average correlation height filter which is capable of discriminating between vessel classes while allowing for intra-class variability. Vessel detections are cross-referenced with e-NOAD data in order to verify the vessel’s access to the port. Our approach does not require foreground/background modeling in order to detect vessels, and therefore it is effective in the presence of the class of dynamic backgrounds, such as moving water, which are prevalent in port facilities. Furthermore, our approach is computationally efficient, thus rendering it more suitable for real-time port surveillance systems. We evaluate our method on a dataset collected from various port locations which contains a wide range of vessel classes.