Deep learning hyper-parameter optimization for video analytics in clouds

Yaseen, Muhammad Usman, Anjum, Ashiq, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 and Antonopoulos, Nikolaos 2019. Deep learning hyper-parameter optimization for video analytics in clouds. IEEE Transactions on Systems Man and Cybernetics: Systems 49 (1) , pp. 253-264. 10.1109/TSMC.2018.2840341

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Abstract

A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, pre-processed and a model for supporting classification is developed on these video streams using cloud-based infrastructure. A key focus in this work 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 hyperparameter tuning improves performance of the proposed pipeline, and outcomes of various parameters on system’s performance is compared. Subsequently, the parameters that contribute towards 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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISSN:	2168-2216
Date of First Compliant Deposit:	16 June 2018
Date of Acceptance:	17 June 2018
Last Modified:	16 Nov 2024 22:00
URI:	https://orca.cardiff.ac.uk/id/eprint/112512

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