Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds

Yaseen, Muhammad Usman; Anjum, Ashiq; Rana, Omer; 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.

• Journal Article Type: Article
• Acceptance Date: Jun 15, 2018
• Online Publication Date: Jun 15, 2018
• Publication Date: 2019-01
• Deposit Date: Feb 12, 2019
• Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems
• Print ISSN: 2168-2216
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: 1
• Pages: 253-264
• DOI: https://doi.org/10.1109/TSMC.2018.2840341
• Keywords: Automatic object classification , cloud computing , deep learning , video analytics
• Public URL: http://researchrepository.napier.ac.uk/Output/1557124