Susan M. Munn

Abstract

Portable eye trackers allow us to see where a subject is looking when performing a natural task with free head and body movements. These eye trackers include headgear containing a camera directed at one of the subject's eyes (the eye camera) and another camera (the scene camera) positioned above the same eye directed along the subject's line-of-sight. The output video includes the scene video with a crosshair depicting where the subject is looking -- the point-of-regard (POR) -- that is updated for each frame. This video may be the desired final result or it may be further analyzed to obtain more specific information about the subject's visual strategies. A list of the calculated POR positions in the scene video can also be analyzed. The goals of this project are to expand the information that we can obtain from a portable video-based monocular eye tracker and to minimize the amount of user interaction required to obtain and analyze this information. This work includes offline processing of both the eye and scene videos to obtain robust 2D PORs in scene video frames, identify gaze fixations from these PORs, obtain 3D head motion and ray trace fixations through volumes-of-interest (VOIs) to determine what is being fixated, when and where (3D POR). To avoid the redundancy of ray tracing a 2D POR in every video frame and to group these POR data meaningfully, a fixation-identification algorithm is employed to simplify the long list of 2D POR data into gaze fixations. In order to ray trace these fixations, the 3D motion -- position and orientation over time -- of the scene camera is computed. This camera motion is determined via an iterative structure and motion recovery algorithm that requires a calibrated camera and knowledge of the 3D location of at least four points in the scene (that can be selected from premeasured VOI vertices). The subjects 3D head motion is obtained directly from this camera motion. For the final stage of the algorithm, the 3D locations and dimensions of VOIs in the scene are required. This VOI information in world coordinates is converted to camera coordinates for ray tracing. A representative 2D POR position for each fixation is converted from image coordinates to the same camera coordinate system. Then, a ray is traced from the camera center through this position to determine which (if any) VOI is being fixated and where it is being fixated -- the 3D POR in the world. Results are presented for various real scenes. Novel visualizations of portable eye tracker data created using the results of our algorithm are also presented.

Library of Congress Subject Headings

Motion perception (Vision)--Computer simulation; Computer vision; Eye--Movements; Video recordings--Data processing

Publication Date

8-10-2009

Document Type

Dissertation

Student Type

Graduate

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Pelz, Jeff

Comments

Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: TA1634 .M86 2009

Campus

RIT – Main Campus

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