A Real-Time Method for Time-to-Collision Estimation from Aerial Images
Large vessels such as container ships rely on experienced pilots with extensive knowledge of the local streams and tides responsible for maneuvering the vessel to its desired location. This work proposes estimating time-to-collision (TTC) between moving objects (i.e., vessels) using real-time video...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/8/3/62 |
| _version_ | 1827648334260600832 |
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| author | Daniel Tøttrup Stinus Lykke Skovgaard Jonas le Fevre Sejersen Rui Pimentel de Figueiredo |
| author_facet | Daniel Tøttrup Stinus Lykke Skovgaard Jonas le Fevre Sejersen Rui Pimentel de Figueiredo |
| author_sort | Daniel Tøttrup |
| collection | DOAJ |
| description | Large vessels such as container ships rely on experienced pilots with extensive knowledge of the local streams and tides responsible for maneuvering the vessel to its desired location. This work proposes estimating time-to-collision (TTC) between moving objects (i.e., vessels) using real-time video data captured from aerial drones in dynamic maritime environments. Our deep-learning-based methods utilize features optimized with realistic virtually generated data for reliable and robust object detection, segmentation, and tracking. Furthermore, we use rotated bounding box representations, obtained from fine semantic segmentation of objects, for enhanced TTC estimation accuracy. We intuitively present collision estimates as collision arrows that gradually change color to red to indicate an imminent collision. Experiments conducted in a realistic dockyard virtual environment show that our approaches precisely, robustly, and efficiently predict TTC between dynamic objects seen from a top-view, with a mean error and a standard deviation of <strong>0.358</strong> and <strong>0.114</strong> s, respectively, in a worst-case scenario. |
| first_indexed | 2024-03-09T19:35:50Z |
| format | Article |
| id | doaj.art-b33ca14194424ba5b1af6bd47d7e99be |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T19:35:50Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-b33ca14194424ba5b1af6bd47d7e99be2023-11-24T01:54:53ZengMDPI AGJournal of Imaging2313-433X2022-03-01836210.3390/jimaging8030062A Real-Time Method for Time-to-Collision Estimation from Aerial ImagesDaniel Tøttrup0Stinus Lykke Skovgaard1Jonas le Fevre Sejersen2Rui Pimentel de Figueiredo3Department of Electrical and Computer Engineering, Aarhus University, Nordre Ringgade 1, 8000 Aarhus, DenmarkDepartment of Electrical and Computer Engineering, Aarhus University, Nordre Ringgade 1, 8000 Aarhus, DenmarkDepartment of Electrical and Computer Engineering, Aarhus University, Nordre Ringgade 1, 8000 Aarhus, DenmarkDepartment of Electrical and Computer Engineering, Aarhus University, Nordre Ringgade 1, 8000 Aarhus, DenmarkLarge vessels such as container ships rely on experienced pilots with extensive knowledge of the local streams and tides responsible for maneuvering the vessel to its desired location. This work proposes estimating time-to-collision (TTC) between moving objects (i.e., vessels) using real-time video data captured from aerial drones in dynamic maritime environments. Our deep-learning-based methods utilize features optimized with realistic virtually generated data for reliable and robust object detection, segmentation, and tracking. Furthermore, we use rotated bounding box representations, obtained from fine semantic segmentation of objects, for enhanced TTC estimation accuracy. We intuitively present collision estimates as collision arrows that gradually change color to red to indicate an imminent collision. Experiments conducted in a realistic dockyard virtual environment show that our approaches precisely, robustly, and efficiently predict TTC between dynamic objects seen from a top-view, with a mean error and a standard deviation of <strong>0.358</strong> and <strong>0.114</strong> s, respectively, in a worst-case scenario.https://www.mdpi.com/2313-433X/8/3/62multiple-object trackingconvolutional neural networkstime-to-collision estimation |
| spellingShingle | Daniel Tøttrup Stinus Lykke Skovgaard Jonas le Fevre Sejersen Rui Pimentel de Figueiredo A Real-Time Method for Time-to-Collision Estimation from Aerial Images Journal of Imaging multiple-object tracking convolutional neural networks time-to-collision estimation |
| title | A Real-Time Method for Time-to-Collision Estimation from Aerial Images |
| title_full | A Real-Time Method for Time-to-Collision Estimation from Aerial Images |
| title_fullStr | A Real-Time Method for Time-to-Collision Estimation from Aerial Images |
| title_full_unstemmed | A Real-Time Method for Time-to-Collision Estimation from Aerial Images |
| title_short | A Real-Time Method for Time-to-Collision Estimation from Aerial Images |
| title_sort | real time method for time to collision estimation from aerial images |
| topic | multiple-object tracking convolutional neural networks time-to-collision estimation |
| url | https://www.mdpi.com/2313-433X/8/3/62 |
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