Over the years, maritime surveillance has become increasingly important due to the recurrence of piracy. While\r\nsurveillance has traditionally been a manual task using crew members in lookout positions on parts of the ship, much\r\nwork is being done to automate this task using digital cameras coupled with a computer that uses image processing\r\ntechniques that intelligently track object in the maritime environment. One such technique is level set segmentation\r\nwhich evolves a contour to objects of interest in a given image. This method works well but gives incorrect\r\nsegmentation results when a target object is corrupted in the image. This paper explores the possibility of factoring in\r\nprior knowledge of a ship�s shape into level set segmentation to improve results, a concept that is unaddressed in\r\nmaritime surveillance problem. It is shown that the developed video tracking system outperforms level set-based\r\nsystems that do not use prior shape knowledge, working well even where these systems fail.
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