Detection and tracking of moving objects in a maritime environment using level set with shape priors

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