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Semi-automatic Anno...
Semi-automatic Annotation of Objects in Visual-Thermal Video
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- Berg, Amanda, 1988- (författare)
- Linköpings universitet,Datorseende,Tekniska fakulteten,Termisk Systemteknik AB, Linköping, Sweden
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- Johnander, Joakim (författare)
- Linköpings universitet,Datorseende,Tekniska fakulteten,Zenuity AB, Göteborg, Sweden
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- Durand de Gevigney, Flavie (författare)
- Linköpings universitet,Datorseende,Tekniska fakulteten,Grenoble INP, France
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- Ahlberg, Jörgen (författare)
- Linköpings universitet,Datorseende,Tekniska fakulteten,Termisk Systemteknik AB, Linköping, Sweden
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- Felsberg, Michael (författare)
- Linköpings universitet,Datorseende,Tekniska fakulteten
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2019
- 2019
- Engelska.
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Ingår i: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728150239 - 9781728150246
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Deep learning requires large amounts of annotated data. Manual annotation of objects in video is, regardless of annotation type, a tedious and time-consuming process. In particular, for scarcely used image modalities human annotationis hard to justify. In such cases, semi-automatic annotation provides an acceptable option.In this work, a recursive, semi-automatic annotation method for video is presented. The proposed method utilizesa state-of-the-art video object segmentation method to propose initial annotations for all frames in a video based on only a few manual object segmentations. In the case of a multi-modal dataset, the multi-modality is exploited to refine the proposed annotations even further. The final tentative annotations are presented to the user for manual correction.The method is evaluated on a subset of the RGBT-234 visual-thermal dataset reducing the workload for a human annotator with approximately 78% compared to full manual annotation. Utilizing the proposed pipeline, sequences are annotated for the VOT-RGBT 2019 challenge.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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