Performance analysis of CNN models for mobile device eye tracking with edge computing

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

8 Citations (Scopus)

Abstract

![CDATA[Eye-tracking is a technique used for determining where users are looking and how long they keep their gaze fixed on a particular location. Developments in mobile technology have made mobile applications pervasive; however, eye tracking on mobile devices is still uncommon. This paper proposes a mobile edge computing architecture for eye tracking. We evaluate four lightweight CNN models (LeNet-5, AlexNet, MobileNet, and ShuffleNet) for gaze estimation on mobile devices using a publicly available dataset called GazeCapture. In order to analyse the feasibility of different inference modes such as on-device, edge-based and cloud-based, we conduct an empirical measurement study to quantify inference time, communication time, and resource consumption in these inference modes. Our analysis indicates that while cloud-based inference provides faster predictions, the communication time between the mobile device and the cloud introduces significant latency into the application. This effectively eliminates the ability to perform real-time eye tracking via cloud inference. Furthermore, our findings show that on-device inference performance is limited by energy and memory consumption, making it unsuitable to provide a high-quality user experience. Additionally, we demonstrated that edge-based inference results in a reasonable response time, memory usage, and energy consumption for eye-tracking applications on mobile devices.]]
Original languageEnglish
Title of host publicationKnowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2022), 7 - 9 September 2022, Verona, Italy
PublisherElsevier
Pages2291-2300
Number of pages10
DOIs
Publication statusPublished - 2022
EventInternational Conference on Knowledge-Based Intelligent Information and Engineering Systems -
Duration: 7 Sept 2022 → …

Conference

ConferenceInternational Conference on Knowledge-Based Intelligent Information and Engineering Systems
Period7/09/22 → …

Open Access - Access Right Statement

© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

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