Ship detection in videos
Computer vision can be used in maritime environment to assist in ship navigation and may lead to reduction in maritime accidents. In this project, improvements were made to an existing ship detection and tracking prototype to solve the issues that the prototype had, such as false positive detections...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2017
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| Online Access: | http://hdl.handle.net/10356/72907 |
| _version_ | 1826115473014521856 |
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| author | Muhammad Mukhtar |
| author2 | Deepu Rajan |
| author_facet | Deepu Rajan Muhammad Mukhtar |
| author_sort | Muhammad Mukhtar |
| collection | NTU |
| description | Computer vision can be used in maritime environment to assist in ship navigation and may lead to reduction in maritime accidents. In this project, improvements were made to an existing ship detection and tracking prototype to solve the issues that the prototype had, such as false positive detections and inability to track occluded objects. Other explorations were done to improve the accuracy of the ship detections. One such exploration is water region segmentation using pixel classification into water and non-water pixels. Classification methods such as linear SVM and random forest were used and feature spaces were built using features such as pixel color features and Gabor pixel texture features. |
| first_indexed | 2024-10-01T03:55:43Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/72907 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:55:43Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/729072023-03-03T20:29:13Z Ship detection in videos Muhammad Mukhtar Deepu Rajan School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering Computer vision can be used in maritime environment to assist in ship navigation and may lead to reduction in maritime accidents. In this project, improvements were made to an existing ship detection and tracking prototype to solve the issues that the prototype had, such as false positive detections and inability to track occluded objects. Other explorations were done to improve the accuracy of the ship detections. One such exploration is water region segmentation using pixel classification into water and non-water pixels. Classification methods such as linear SVM and random forest were used and feature spaces were built using features such as pixel color features and Gabor pixel texture features. Bachelor of Engineering (Computer Science) 2017-12-12T06:34:16Z 2017-12-12T06:34:16Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/72907 en Nanyang Technological University 50 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Muhammad Mukhtar Ship detection in videos |
| title | Ship detection in videos |
| title_full | Ship detection in videos |
| title_fullStr | Ship detection in videos |
| title_full_unstemmed | Ship detection in videos |
| title_short | Ship detection in videos |
| title_sort | ship detection in videos |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/72907 |
| work_keys_str_mv | AT muhammadmukhtar shipdetectioninvideos |