Abstract
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.
Highlights
While the word ‘pirate’ brings to mind thoughts of the swashbuckling, one-eyed seafarers of childhood fantasy, the term still, has use in today’s modern world
Outcomes where the system agrees with the actual data are labelled true positives (TP) or true negatives (TN) depending on whether the pixel belongs to an object or not
If the system incorrectly labels a pixel as an object when in actuality there is not one there, this is called a false positive (FP), while a false negative (FN) is a case where an object is present but the system fails to detect it
Summary
While the word ‘pirate’ brings to mind thoughts of the swashbuckling, one-eyed seafarers of childhood fantasy, the term still, has use in today’s modern world. In [5], temporal characteristics of sea clutter and that of a range of small boats are analysed using a comprehensive set of recorded datasets. This is done in an attempt to understand the dynamics and associated reflexivity of small boats. It allows the development of advanced detection and tracking algorithms, which will help improve the performance of surveillance and marine navigation radar against small boats. Once the image has been filtered, the system detects objects using a background modelling and subtraction algorithm. When a new pixel value It is observed, the probability of its value is calculated from this density estimate.
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