Deep Learning based Super-Resolution for Improved Action Recognition

Kamal Nasrollahi, Sergio Escalera Guerrero, Pejman Rasti, Gholamreza Anbarjafari, Xavier Baro, Hugo J. Escalante, Thomas B. Moeslund

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18 Citationer (Scopus)
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Resumé

Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging bench- mark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-of- the-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos.
OriginalsprogEngelsk
TitelInternational Conference on Image Processing Theory, Tools and Applications (IPTA), 2015
Antal sider6
ForlagIEEE Signal Processing Society
Publikationsdato2015
Sider67 - 72
ISBN (Trykt)978-1-4799-8636-1, 978-1-4799-8637-8
DOI
StatusUdgivet - 2015
BegivenhedIEEE International Conference on Image Processing Theory, Tools and Applications - Orleans, Frankrig
Varighed: 10 nov. 201513 nov. 2015
Konferencens nummer: 5th

Konference

KonferenceIEEE International Conference on Image Processing Theory, Tools and Applications
Nummer5th
LandFrankrig
ByOrleans
Periode10/11/201513/11/2015

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Cameras
Interpolation
Deep learning

Citer dette

Nasrollahi, K., Guerrero, S. E., Rasti, P., Anbarjafari, G., Baro, X., J. Escalante, H., & Moeslund, T. B. (2015). Deep Learning based Super-Resolution for Improved Action Recognition. I International Conference on Image Processing Theory, Tools and Applications (IPTA), 2015 (s. 67 - 72). IEEE Signal Processing Society. https://doi.org/10.1109/IPTA.2015.7367098
Nasrollahi, Kamal ; Guerrero, Sergio Escalera ; Rasti, Pejman ; Anbarjafari, Gholamreza ; Baro, Xavier ; J. Escalante, Hugo ; Moeslund, Thomas B. / Deep Learning based Super-Resolution for Improved Action Recognition. International Conference on Image Processing Theory, Tools and Applications (IPTA), 2015. IEEE Signal Processing Society, 2015. s. 67 - 72
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title = "Deep Learning based Super-Resolution for Improved Action Recognition",
abstract = "Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging bench- mark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-of- the-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos.",
author = "Kamal Nasrollahi and Guerrero, {Sergio Escalera} and Pejman Rasti and Gholamreza Anbarjafari and Xavier Baro and {J. Escalante}, Hugo and Moeslund, {Thomas B.}",
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Nasrollahi, K, Guerrero, SE, Rasti, P, Anbarjafari, G, Baro, X, J. Escalante, H & Moeslund, TB 2015, Deep Learning based Super-Resolution for Improved Action Recognition. i International Conference on Image Processing Theory, Tools and Applications (IPTA), 2015. IEEE Signal Processing Society, s. 67 - 72, IEEE International Conference on Image Processing Theory, Tools and Applications , Orleans, Frankrig, 10/11/2015. https://doi.org/10.1109/IPTA.2015.7367098

Deep Learning based Super-Resolution for Improved Action Recognition. / Nasrollahi, Kamal; Guerrero, Sergio Escalera; Rasti, Pejman; Anbarjafari, Gholamreza; Baro, Xavier; J. Escalante, Hugo; Moeslund, Thomas B.

International Conference on Image Processing Theory, Tools and Applications (IPTA), 2015. IEEE Signal Processing Society, 2015. s. 67 - 72.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

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AU - Nasrollahi, Kamal

AU - Guerrero, Sergio Escalera

AU - Rasti, Pejman

AU - Anbarjafari, Gholamreza

AU - Baro, Xavier

AU - J. Escalante, Hugo

AU - Moeslund, Thomas B.

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AB - Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging bench- mark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-of- the-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos.

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M3 - Article in proceeding

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Nasrollahi K, Guerrero SE, Rasti P, Anbarjafari G, Baro X, J. Escalante H et al. Deep Learning based Super-Resolution for Improved Action Recognition. I International Conference on Image Processing Theory, Tools and Applications (IPTA), 2015. IEEE Signal Processing Society. 2015. s. 67 - 72 https://doi.org/10.1109/IPTA.2015.7367098