Maritime tracking using level sets with shape priors.
| dc.contributor.advisor | Tapamo, Jules-Raymond. | |
| dc.contributor.advisor | Bachoo, Asheer Kasar. | |
| dc.contributor.advisor | Peplow, Roger Charles Samuel. | |
| dc.contributor.author | Frost, Duncan Peter. | |
| dc.date.accessioned | 2013-05-10T07:34:35Z | |
| dc.date.available | 2013-05-10T07:34:35Z | |
| dc.date.created | 2012 | |
| dc.date.issued | 2012 | |
| dc.description | Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2012. | en |
| dc.description.abstract | Piracy is still a significant threat to ships in a maritime environment. Areas such as the coast of Somalia and the Strait of Malacca are still plagued by pirates, and the total international cost of piracy numbers in the billions of dollars. The first line of defence against these threats is early detection and thus maritime surveillance has become an increasingly important task over the years. 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 equipped with computer vision software. While these systems are beneficial in that they do not grow tired like their human counterparts, the maritime environment is a challenging task for computer vision systems. This dissertation aims to address some of these challenges by presenting a system that is able to use prior knowledge of an object’s shape to aid in detection and tracking of the object. Additionally, it aims to test this system under various environmental conditions (such as weather). The system is based around the segmentation technique known as the level set method, which uses a contour in the image that is evolved to separate regions of interest. The system is split into two parts, comprising of an object detection stage that initially finds objects in a scene, and an object tracking stage that tracks detected objects for the rest of the sequence. The object detection stage uses a kernel density estimation-based background subtraction and a binary image level set filter, while the object tracker makes use of a tracking level set algorithm for its functionality. The object detector was tested using a group of 4 sequences, of which it was able to find a prior-known object in 3. The object tracker was tested on a group of 10 sequences for 300 frames a sequence. In 6 of these sequences the object tracker was able to successfully track the object in every single frame. It is shown that the developed video tracking system outperforms level set–based systems that don’t use prior shape knowledge, working well even where these systems fail. | en |
| dc.identifier.uri | http://hdl.handle.net/10413/8858 | |
| dc.language.iso | en_ZA | en |
| dc.subject | Shipping--Security measures. | en |
| dc.subject | Electronic surveillance. | en |
| dc.subject | Level set methods. | en |
| dc.subject | Theses--Electronic engineering. | en |
| dc.title | Maritime tracking using level sets with shape priors. | en |
| dc.type | Thesis | en |