Deep learning hyper-parameter optimization for video analytics in clouds.

Journal article


Yaseen, M., Anjum, Ashiq, Rana, Omer and Antonopoulos, Nikolaos 2018. Deep learning hyper-parameter optimization for video analytics in clouds. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/TSMC.2018.2840341
AuthorsYaseen, M., Anjum, Ashiq, Rana, Omer and Antonopoulos, Nikolaos
Abstract

A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, preprocessed, and a model for supporting classification is developed on these video streams using cloud-based infrastructure. A key focus in this paper is on tuning hyper-parameters associated with the deep learning algorithm used to construct the model. We further propose an automatic video object classification pipeline to validate the system. The mathematical model used to support hyper-parameter tuning improves performance of the proposed pipeline, and outcomes of various parameters on system's performance is compared. Subsequently, the parameters that contribute toward the most optimal performance are selected for the video object classification pipeline. Our experiment-based validation reveals an accuracy and precision of 97% and 96%, respectively. The system proved to be scalable, robust, and customizable for a variety of different applications.

KeywordsAutomatic object classification; Cloud computing; Video analysis; Deep learning
Year2018
JournalIEEE Transactions on Systems, Man, and Cybernetics
PublisherIEEE
ISSN21682216
21682232
Digital Object Identifier (DOI)https://doi.org/10.1109/TSMC.2018.2840341
Web address (URL)http://hdl.handle.net/10545/622767
hdl:10545/622767
Publication dates15 Jun 2018
Publication process dates
Deposited27 Jun 2018, 08:19
Accepted09 May 2018
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Archived with thanks to IEEE Transactions on Systems, Man, and Cybernetics: Systems

ContributorsUniversity of Derby and Cardiff University
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