Extreme Low-Resolution Activity Recognition Using a Super-Resolution-Oriented Generative Adversarial Network Article Swipe
Mingzheng Hou
,
Song Liu
,
Jiliu Zhou
,
Yi Zhang
,
Ziliang Feng
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/mi12060670
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/mi12060670
Activity recognition is a fundamental and crucial task in computer vision. Impressive results have been achieved for activity recognition in high-resolution videos, but for extreme low-resolution videos, which capture the action information at a distance and are vital for preserving privacy, the performance of activity recognition algorithms is far from satisfactory. The reason is that extreme low-resolution (e.g., 12 × 16 pixels) images lack adequate scene and appearance information, which is needed for efficient recognition. To address this problem, we propose a super-resolution-driven generative adversarial network for activity recognition. To fully take advantage of the latent information in low-resolution images, a powerful network module is employed to super-resolve the extremely low-resolution images with a large scale factor. Then, a general activity recognition network is applied to analyze the super-resolved video clips. Extensive experiments on two public benchmarks were conducted to evaluate the effectiveness of our proposed method. The results demonstrate that our method outperforms several state-of-the-art low-resolution activity recognition approaches.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/mi12060670
- https://www.mdpi.com/2072-666X/12/6/670/pdf?version=1623212894
- OA Status
- gold
- Cited By
- 11
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3171471732
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3171471732Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/mi12060670Digital Object Identifier
- Title
-
Extreme Low-Resolution Activity Recognition Using a Super-Resolution-Oriented Generative Adversarial NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-08Full publication date if available
- Authors
-
Mingzheng Hou, Song Liu, Jiliu Zhou, Yi Zhang, Ziliang FengList of authors in order
- Landing page
-
https://doi.org/10.3390/mi12060670Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-666X/12/6/670/pdf?version=1623212894Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-666X/12/6/670/pdf?version=1623212894Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Generative adversarial network, Low resolution, Resolution (logic), Computer vision, Generative grammar, Activity recognition, Adversarial system, Pattern recognition (psychology), Superresolution, Image (mathematics), High resolution, Remote sensing, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2024: 5, 2023: 3, 2022: 2, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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