Cloud-based video analytics using convolutional neural networks

Yaseen, Muhammad Usman; Anjum, Ashiq; Farid, Mohsen; 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.

Citation

Yaseen, M. U., Anjum, A., Farid, M., & Antonopoulos, N. (2019). Cloud-based video analytics using convolutional neural networks. Software: Practice and Experience, 49(4), 565-583. https://doi.org/10.1002/spe.2636

• Journal Article Type: Article
• Acceptance Date: Aug 6, 2018
• Online Publication Date: Sep 13, 2018
• Publication Date: 2019-04
• Deposit Date: Feb 12, 2019
• Journal: Software: Practice and Experience
• Print ISSN: 0038-0644
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: 4
• Pages: 565-583
• DOI: https://doi.org/10.1002/spe.2636
• Keywords: Cloud computing, convolutional neural networks, deep learning, hyperparameter tuning, video analytics,
• Public URL: http://researchrepository.napier.ac.uk/Output/1557093