Deep learning algorithms and applications

This paper presents an appearance-based gaze-tracking implementation called Browser Eye Tracker (BET). BET is a convolutional neural network for real-time (>30fps) eye- tracking that can run on any device with a web browser without first downloading anything or buying specialised eye-tracking web...

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Bibliographic Details
Main Author:	Ong, Yu Fei
Other Authors:	Tan Yap Peng
Format:	Final Year Project
Language:	English
Published:	2019
Subjects:	DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Online Access:	http://hdl.handle.net/10356/77447
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-774472023-07-07T17:16:55Z Deep learning algorithms and applications Ong, Yu Fei Tan Yap Peng School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence This paper presents an appearance-based gaze-tracking implementation called Browser Eye Tracker (BET). BET is a convolutional neural network for real-time (>30fps) eye- tracking that can run on any device with a web browser without first downloading anything or buying specialised eye-tracking webcams. BET achieves a prediction error 2% lower than previous in-browser approaches on average. An in-browser Auto Sampler (AS) for automated sample collection, a Gaze-tracking Playground (GP) for comparing different models and Real-Time Prediction Testing (RTPT) were also implemented as part of the project. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-05-29T04:20:28Z 2019-05-29T04:20:28Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77447 en Nanyang Technological University 45 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle	DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Ong, Yu Fei Deep learning algorithms and applications
description	This paper presents an appearance-based gaze-tracking implementation called Browser Eye Tracker (BET). BET is a convolutional neural network for real-time (>30fps) eye- tracking that can run on any device with a web browser without first downloading anything or buying specialised eye-tracking webcams. BET achieves a prediction error 2% lower than previous in-browser approaches on average. An in-browser Auto Sampler (AS) for automated sample collection, a Gaze-tracking Playground (GP) for comparing different models and Real-Time Prediction Testing (RTPT) were also implemented as part of the project.
author2	Tan Yap Peng
author_facet	Tan Yap Peng Ong, Yu Fei
format	Final Year Project
author	Ong, Yu Fei
author_sort	Ong, Yu Fei
title	Deep learning algorithms and applications
title_short	Deep learning algorithms and applications
title_full	Deep learning algorithms and applications
title_fullStr	Deep learning algorithms and applications
title_full_unstemmed	Deep learning algorithms and applications
title_sort	deep learning algorithms and applications
publishDate	2019
url	http://hdl.handle.net/10356/77447
_version_	1772825160995307520

Deep learning algorithms and applications

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