Year: 2016
Type: conference-paper
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-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 complex static environments such as crime scenes.
Institutions: Rochester Institute of Technology, West Virginia University
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
OpenAlex ID: W2298576963