Harnessing the Power of Self-Training for Gaze Point Estimation in Dual Camera Transportation Datasets

This thesis proposes a novel approach for efficiently estimating gaze points in dual camera transportation datasets. Traditional methods for gaze point estimation are dependent on large amounts of labeled data, which can be both expensive and time-consuming to collect. Additionally, alignment and calibration of the two camera views present significant challenges. To overcome these limitations, this thesis investigates the use of self-learning techniques such as semi-supervised learning and self-training, which can reduce the need for labeled data while maintaining high accuracy. The proposed method is evaluated on the DGAZE dataset and achieves a 57.2\% improvement in performance compared to the previous methods. This approach can prove to be a valuable tool for studying visual attention in transportation research, leading to more cost-effective and efficient research in this field.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Bhagat, Hirva Alpesh

Chairs dc:contributor.committeechair

• Karpatne, Anuj
• Abbott, Amos L.

Committee members dc:contributor.committeemember

• Sarkar, Abhijit
• Fox, Edward A.

• Point of gaze
• gaze point estimation
• self training
• semi-supervised learning
• driver safety

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:37781

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/115430