Deep learning algorithms and applications

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
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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