Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Deep learning hyper-parameter optimization for video analytics in clouds

Yaseen, Muhammad Usman, 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: Systems 10.1109/TSMC.2018.2840341

[img]
Preview
PDF - Accepted Post-Print Version
Download (3MB) | Preview

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: In Press
Schools: Computer Science & Informatics
Publisher: IEEE
ISSN: 2168-2216
Last Modified: 20 Jun 2018 04:01
URI: http://orca.cf.ac.uk/id/eprint/112512

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics