Journal article
Multiple-model fully convolutional neural networks for single object tracking on thermal infrared video
- Abstract:
-
The availability of affordable thermal infrared (TIR) camera has instigated its usage in various research fields, especially for the cases that require images to be captured in dark surroundings. One of the low-level tasks required by most TIR-based researches is the need to track an object throughout a video sequence. The main challenge posed by TIR camera usage is the lack of texture to differentiate two nearby objects of the same class. According to the VOT-TIR 2016 challenge, the best ful...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Authors
Bibliographic Details
- Publisher:
- IEEE Publisher's website
- Journal:
- IEEE Access Journal website
- Volume:
- 6
- Pages:
- 42790-42799
- Publication date:
- 2018-07-25
- Acceptance date:
- 2018-07-22
- DOI:
- EISSN:
-
2169-3536
- ISSN:
-
2169-3536
Item Description
- Keywords:
- Pubs id:
-
pubs:892603
- UUID:
-
uuid:0d504698-7b2c-4af2-bd4b-ede08a17bb85
- Local pid:
- pubs:892603
- Source identifiers:
-
892603
- Deposit date:
- 2019-08-23
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2018
- Notes:
- © 2018 IEEE. Translations and content mining are permitted for academic research only.Personal use is also permitted, but republication/redistribution requires IEEE permission. This publication is an Open Access only journal. Open Access provides unrestricted online access to peer-reviewed journal articles.
- Licence:
- CC Attribution (CC BY)
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