Tracking and Localization based on Multi-angle Vision for Underwater Target
With the cost reduction of underwater sensor network nodes and the increasing demand for underwater detection and monitoring, near-land areas, shallow water areas, lakes and rivers have gradually tended to densely arranged sensor nodes. In order to achieve real-time monitoring, most nodes now have v...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/11/1871 |
| _version_ | 1797548568561582080 |
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| author | Jun Liu Shenghua Gong Wenxue Guan Benyuan Li Haobo Li Jiaxin Liu |
| author_facet | Jun Liu Shenghua Gong Wenxue Guan Benyuan Li Haobo Li Jiaxin Liu |
| author_sort | Jun Liu |
| collection | DOAJ |
| description | With the cost reduction of underwater sensor network nodes and the increasing demand for underwater detection and monitoring, near-land areas, shallow water areas, lakes and rivers have gradually tended to densely arranged sensor nodes. In order to achieve real-time monitoring, most nodes now have visual sensors instead of acoustic sensors to collect and analyze optical images, mainly because cameras might be more advantageous when it comes to dense underwater sensor networks. In this article, image enhancement, saliency detection, calibration and refraction model calculation are performed on the video streams collected by multiple optical cameras to obtain the track of the dynamic target. This study not only innovatively combines the application of AOD-Net’s (all-in-one network) image defogging algorithm with underwater image enhancement, but also refers to the BASNet (Boundary-Aware Salient network) network architecture, introducing frame difference results in the input to reduce the interference of static targets. Based on the aforementioned technologies, this paper designs a dynamic target tracking system centered on video stream processing in dense underwater networks. As part of the process, most nodes carried underwater cameras. When the dynamic target could be captured by at least two nodes in the network at the same time, the target position could then be calculated and tracked. |
| first_indexed | 2024-03-10T15:01:23Z |
| format | Article |
| id | doaj.art-b5c140ee0b5b4c6e942bd69440f4357d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T15:01:23Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b5c140ee0b5b4c6e942bd69440f4357d2023-11-20T20:08:29ZengMDPI AGElectronics2079-92922020-11-01911187110.3390/electronics9111871Tracking and Localization based on Multi-angle Vision for Underwater TargetJun Liu0Shenghua Gong1Wenxue Guan2Benyuan Li3Haobo Li4Jiaxin Liu5College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaWith the cost reduction of underwater sensor network nodes and the increasing demand for underwater detection and monitoring, near-land areas, shallow water areas, lakes and rivers have gradually tended to densely arranged sensor nodes. In order to achieve real-time monitoring, most nodes now have visual sensors instead of acoustic sensors to collect and analyze optical images, mainly because cameras might be more advantageous when it comes to dense underwater sensor networks. In this article, image enhancement, saliency detection, calibration and refraction model calculation are performed on the video streams collected by multiple optical cameras to obtain the track of the dynamic target. This study not only innovatively combines the application of AOD-Net’s (all-in-one network) image defogging algorithm with underwater image enhancement, but also refers to the BASNet (Boundary-Aware Salient network) network architecture, introducing frame difference results in the input to reduce the interference of static targets. Based on the aforementioned technologies, this paper designs a dynamic target tracking system centered on video stream processing in dense underwater networks. As part of the process, most nodes carried underwater cameras. When the dynamic target could be captured by at least two nodes in the network at the same time, the target position could then be calculated and tracked.https://www.mdpi.com/2079-9292/9/11/1871underwater sensors networkvideo saliency detectiondynamic target tracking and localization |
| spellingShingle | Jun Liu Shenghua Gong Wenxue Guan Benyuan Li Haobo Li Jiaxin Liu Tracking and Localization based on Multi-angle Vision for Underwater Target Electronics underwater sensors network video saliency detection dynamic target tracking and localization |
| title | Tracking and Localization based on Multi-angle Vision for Underwater Target |
| title_full | Tracking and Localization based on Multi-angle Vision for Underwater Target |
| title_fullStr | Tracking and Localization based on Multi-angle Vision for Underwater Target |
| title_full_unstemmed | Tracking and Localization based on Multi-angle Vision for Underwater Target |
| title_short | Tracking and Localization based on Multi-angle Vision for Underwater Target |
| title_sort | tracking and localization based on multi angle vision for underwater target |
| topic | underwater sensors network video saliency detection dynamic target tracking and localization |
| url | https://www.mdpi.com/2079-9292/9/11/1871 |
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