Cloud-based video analytics using convolutional neural networks.

Journal article


Yaseen, M., Anjum, Ashiq, Farid, Mohsen and Antonopoulos, Nick 2018. Cloud-based video analytics using convolutional neural networks. Software Practice and Experience. https://doi.org/10.1002/spe.2636
AuthorsYaseen, M., Anjum, Ashiq, Farid, Mohsen and Antonopoulos, Nick
Abstract

Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management. Traditional video analytics systems work on shallow networks and are unable to harness the power of distributed processing for training and inference. We propose a cloud‐based video analytics system based on an optimally tuned convolutional neural network to classify objects from video streams. The tuning of convolutional neural network is empowered by in‐memory distributed computing. The object classification is performed by comparing the target object with the prestored trained patterns, generating a set of matching scores. The matching scores greater than an empirically determined threshold reveal the classification of the target object. The proposed system proved to be robust to classification errors with an accuracy and precision of 97% and 96%, respectively, and can be used as a general‐purpose video analytics system.

KeywordsDeep learning; Video analysis; Neural networks; Cloud Computing
Year2018
JournalSoftware Practice and Experience
ISSN0038-0644
Digital Object Identifier (DOI)https://doi.org/10.1002/spe.2636
Web address (URL)http://hdl.handle.net/10545/623037
hdl:10545/623037
Publication dates13 Sep 2018
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Deposited12 Oct 2018, 14:29
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ContributorsUniversity of Derby, Department of Electronics, Computing and Mathematics; University of Derby; Derby UK, Department of Electronics, Computing and Mathematics; University of Derby; Derby UK, Department of Electronics, Computing and Mathematics; University of Derby; Derby UK and Department of Electronics, Computing and Mathematics; University of Derby; Derby UK
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