Cloud-Based ImageNet Object Recognition For Mobile Devices

Saeed, Akram and Schonfeld, Dan (2020) Cloud-Based ImageNet Object Recognition For Mobile Devices. In: IMDC-SDSP 2020, 28-30 June 2020, Cyberspace.

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Abstract

User reliance on real-time applications is continuously increasing as the use of smartphone devices has tremendously increased in day to day life within the past few years. Smartphone devices computation power has significantly increased as well, however, there are still some scalability, performance challenges and some complications with real-time application such as limited computation capabilities and battery life of mobile devices. In this paper, we propose a cloud-based object recognition through task offloading to a high-speed server. We explore this design extensively and demonstrate a real-time solution for object recognition framework on mobile devices using a Convolution neural network (CNN) leveraging the ImageNet dataset and by optimizing the offloading process to minimize the time and energy needed. This framework use the emerging the Android operating system as a platform to connect with an object recognition server where the CNN deep learning resides and process received images. Using this method will overcome the design limited capacity of mobile devices since object recognition algorithms require high-speed calculations

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: cloud computing convolutional neural network object recognition
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: EAI Editor III.
Date Deposited: 26 Feb 2021 16:15
Last Modified: 26 Feb 2021 16:15
URI: https://eprints.eudl.eu/id/eprint/1086

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