Abstract

We present a novel pipeline for localizing a free roaming eye tracker within a LiDAR-based 3D reconstructed scene with high levels of accuracy. By utilizing a combination of reconstruction algorithms that leverage the strengths of global versus local capture methods and user-assisted refinement, we reduce drift errors associated with Dense Simultaneous Localization and Mapping (D-SLAM) techniques. Our framework supports region-of-interest (ROI) annotation and gaze statistics generation and the ability to visualize gaze in 3D from an immersive first person or third person perspective. This approach gives unique insights into viewers' problem solving and search task strategies and has high applicability in indoor static environments such as crime scenes.

Library of Congress Subject Headings

Eye tracking--Data processing; Optical radar; Three-dimensional imaging

Publication Date

12-2015

Document Type

Thesis

Student Type

Graduate

Degree Name

Computer Science (MS)

Department, Program, or Center

Computer Science (GCCIS)

Advisor

Reynold Bailey

Advisor/Committee Member

Joe Geigel

Advisor/Committee Member

Srinivas Sridharan

Comments

Physical copy available from RIT's Wallace Library at QP477.5 .P43 2015

Campus

RIT – Main Campus

Plan Codes

COMPSCI-MS

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