On-Device Eye Tracking System with Dual Lightweight AI Processor

Jongwon Oh, Raehyeong Kim, Jinyeol Kim, Seung Eun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the growth of personalized devices, in various field, eye tracking research is conducted for enhancing user experience. Also, the recent research focuses on the method based on artificial intelligence (AI) algorithm for improving the performance. However, the eye tracking through AI algorithm needs significant resources. For this reason, the AI-based methods have limitations applying to embedded system. For solving the problems, in this paper, we propose an eye tracking system with two AI processors based on light weight algorithm, k-nearest neighbor (k-NN). The processor was implemented on a field programmable gate array (FPGA), and the eye tracking system was verified through visible light eye images. The results demonstrate the feasibility of the proposed eye tracking system.

Original languageEnglish
Title of host publicationProceedings - International SoC Design Conference 2024, ISOCC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages344-345
Number of pages2
ISBN (Electronic)9798350377088
DOIs
StatePublished - 2024
Event21st International System-on-Chip Design Conference, ISOCC 2024 - Sapporo, Japan
Duration: 19 Aug 202422 Aug 2024

Publication series

NameProceedings - International SoC Design Conference 2024, ISOCC 2024

Conference

Conference21st International System-on-Chip Design Conference, ISOCC 2024
Country/TerritoryJapan
CitySapporo
Period19/08/2422/08/24

Keywords

  • aritificial intelligence
  • embedded system
  • eye tracking

Fingerprint

Dive into the research topics of 'On-Device Eye Tracking System with Dual Lightweight AI Processor'. Together they form a unique fingerprint.

Cite this