@InProceedings{KamalNasrollahi2015,
author="Kamal Nasrollahi
and Sergio Escalera
and P. Rasti
and Gholamreza Anbarjafari
and Xavier Baro
and Hugo Jair Escalante
and Thomas B. Moeslund",
title="Deep Learning based Super-Resolution for Improved Action Recognition",
booktitle="5th International Conference on Image Processing Theory, Tools and Applications IPTA2015",
year="2015",
pages="67--72",
abstract="Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging benchmark 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-ofthe-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.",
optnote="HuPBA;MV",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2648), last updated on Mon, 15 May 2017 12:14:01 +0200",
doi="10.1109/IPTA.2015.7367098",
file=":http://refbase.cvc.uab.es/files/NER2015.pdf:PDF"
}