Learning to Track Aircraft in Infrared Imagery
Airborne target tracking in infrared imagery remains a challenging task. The airborne target usually has a low signal-to-noise ratio and shows different visual patterns. The features adopted in the visual tracking algorithm are usually deep features pre-trained on ImageNet, which are not tightly cou...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/23/3995 |
| _version_ | 1827604517608226816 |
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| author | Sijie Wu Kai Zhang Shaoyi Li Jie Yan |
| author_facet | Sijie Wu Kai Zhang Shaoyi Li Jie Yan |
| author_sort | Sijie Wu |
| collection | DOAJ |
| description | Airborne target tracking in infrared imagery remains a challenging task. The airborne target usually has a low signal-to-noise ratio and shows different visual patterns. The features adopted in the visual tracking algorithm are usually deep features pre-trained on ImageNet, which are not tightly coupled with the current video domain and therefore might not be optimal for infrared target tracking. To this end, we propose a new approach to learn the domain-specific features, which can be adapted to the current video online without pre-training on a large datasets. Considering that only a few samples of the initial frame can be used for online training, general feature representations are encoded to the network for a better initialization. The feature learning module is flexible and can be integrated into tracking frameworks based on correlation filters to improve the baseline method. Experiments on airborne infrared imagery are conducted to demonstrate the effectiveness of our tracking algorithm. |
| first_indexed | 2024-03-09T06:01:09Z |
| format | Article |
| id | doaj.art-501ca0c1d559460cb414524309439978 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T06:01:09Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-501ca0c1d559460cb4145243094399782023-12-03T12:08:51ZengMDPI AGRemote Sensing2072-42922020-12-011223399510.3390/rs12233995Learning to Track Aircraft in Infrared ImagerySijie Wu0Kai Zhang1Shaoyi Li2Jie Yan3School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaAirborne target tracking in infrared imagery remains a challenging task. The airborne target usually has a low signal-to-noise ratio and shows different visual patterns. The features adopted in the visual tracking algorithm are usually deep features pre-trained on ImageNet, which are not tightly coupled with the current video domain and therefore might not be optimal for infrared target tracking. To this end, we propose a new approach to learn the domain-specific features, which can be adapted to the current video online without pre-training on a large datasets. Considering that only a few samples of the initial frame can be used for online training, general feature representations are encoded to the network for a better initialization. The feature learning module is flexible and can be integrated into tracking frameworks based on correlation filters to improve the baseline method. Experiments on airborne infrared imagery are conducted to demonstrate the effectiveness of our tracking algorithm.https://www.mdpi.com/2072-4292/12/23/3995feature learningcorrelation filtersaircraft trackinginfrared imagery |
| spellingShingle | Sijie Wu Kai Zhang Shaoyi Li Jie Yan Learning to Track Aircraft in Infrared Imagery Remote Sensing feature learning correlation filters aircraft tracking infrared imagery |
| title | Learning to Track Aircraft in Infrared Imagery |
| title_full | Learning to Track Aircraft in Infrared Imagery |
| title_fullStr | Learning to Track Aircraft in Infrared Imagery |
| title_full_unstemmed | Learning to Track Aircraft in Infrared Imagery |
| title_short | Learning to Track Aircraft in Infrared Imagery |
| title_sort | learning to track aircraft in infrared imagery |
| topic | feature learning correlation filters aircraft tracking infrared imagery |
| url | https://www.mdpi.com/2072-4292/12/23/3995 |
| work_keys_str_mv | AT sijiewu learningtotrackaircraftininfraredimagery AT kaizhang learningtotrackaircraftininfraredimagery AT shaoyili learningtotrackaircraftininfraredimagery AT jieyan learningtotrackaircraftininfraredimagery |