Detection and tracking of small targets in maritime infrared imagery
Infrared small and dim target detection is a key technology which is extensively used in military reconnaissance, navigation, security surveillance, missile guidance and other applications. But the detection of an object in maritime environment in infra-red images is a challenging task in computer...
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sg-ntu-dr.10356-667322023-03-03T20:29:52Z Detection and tracking of small targets in maritime infrared imagery Muruganandan, Raghavi Deepu Rajan School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Infrared small and dim target detection is a key technology which is extensively used in military reconnaissance, navigation, security surveillance, missile guidance and other applications. But the detection of an object in maritime environment in infra-red images is a challenging task in computer vision and image processing due to the presence of random background noise, small and dim targets, lack of information regarding the shape or texture of the target, etc. However, with the help of a combination of various image processing techniques, it is possible to detect the small moving targets in maritime background. In this report, an algorithm is developed to detect distant targets (ships, boats) and track them in maritime infrared image sequences. As the small and dim targets are present at a very far distance from the infrared imaging system, they lie close to the sky-sea line. Prior to the identification of the sky-sea line, the image is converted to grayscale and a combination of normalized box filter and Canny edge detector is used to detect the edges in the image. The sky-sea line is then identified by finding the longest connected component/edge amongst the detected edges. As the target appears fused with the sky-sea line, a morphological erosion operation is performed to extract the target followed by clustering to group the remaining white pixels into clusters, after which a red box is drawn around the largest cluster (target). The algorithm performs fairly well in detecting and tracking the target when applied to two datasets, each consisting of 500 frames. Bachelor of Engineering (Computer Engineering) 2016-04-25T01:42:42Z 2016-04-25T01:42:42Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/66732 en Nanyang Technological University application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Muruganandan, Raghavi Detection and tracking of small targets in maritime infrared imagery |
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Infrared small and dim target detection is a key technology which is extensively used in military reconnaissance, navigation, security surveillance, missile guidance and other applications.
But the detection of an object in maritime environment in infra-red images is a challenging task in computer vision and image processing due to the presence of random background noise, small and dim targets, lack of information regarding the shape or texture of the target, etc. However, with the help of a combination of various image processing techniques, it is possible to detect the small moving targets in maritime background. In this report, an algorithm is developed to detect distant targets (ships, boats) and track them in maritime infrared image sequences. As the small and dim targets are present at a very far distance from the infrared imaging system, they lie close to the sky-sea line. Prior to the identification of the sky-sea line, the image is converted to grayscale and a combination of normalized box filter and Canny edge detector is used to detect the edges in the image. The sky-sea line is then identified by finding the longest connected component/edge amongst the detected edges. As the target appears fused with the sky-sea line, a morphological erosion operation is performed to extract the target followed by clustering to group the remaining white pixels into clusters, after which a red box is drawn around the largest cluster (target). The algorithm performs fairly well in detecting and tracking the target when applied to two datasets, each consisting of 500 frames. |
author2 |
Deepu Rajan |
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Deepu Rajan Muruganandan, Raghavi |
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Final Year Project |
author |
Muruganandan, Raghavi |
author_sort |
Muruganandan, Raghavi |
title |
Detection and tracking of small targets in maritime infrared imagery |
title_short |
Detection and tracking of small targets in maritime infrared imagery |
title_full |
Detection and tracking of small targets in maritime infrared imagery |
title_fullStr |
Detection and tracking of small targets in maritime infrared imagery |
title_full_unstemmed |
Detection and tracking of small targets in maritime infrared imagery |
title_sort |
detection and tracking of small targets in maritime infrared imagery |
publishDate |
2016 |
url |
http://hdl.handle.net/10356/66732 |
_version_ |
1759854427374616576 |