3D head motion, point-of-regard and encoded gaze fixations in real scenes: next-generation portable video-based monocular eye tracking

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dc.contributor.advisor Pelz, Jeff
dc.contributor.author Munn, Susan M.
dc.date.accessioned 2010-01-29T18:05:40Z
dc.date.available 2010-01-29T18:05:40Z
dc.date.issued 2009-08-10
dc.identifier.uri http://hdl.handle.net/1850/11206
dc.description.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. en_US
dc.language.iso en_US en_US
dc.subject Computer en_US
dc.subject Eye en_US
dc.subject Processing en_US
dc.subject Tracking en_US
dc.subject Video en_US
dc.subject Vision en_US
dc.subject.lcc TA1634 .M86 2009
dc.subject.lcsh Motion perception (Vision)--Computer simulation en_US
dc.subject.lcsh Computer vision en_US
dc.subject.lcsh Eye--Movements en_US
dc.subject.lcsh Video recordings--Data processing en_US
dc.title 3D head motion, point-of-regard and encoded gaze fixations in real scenes: next-generation portable video-based monocular eye tracking en_US
dc.type Dissertation en_US
dc.description.college College of Science en_US
dc.description.department Chester F. Carlson Center for Imaging Science en_US
dc.description.school Rochester Institute of Technology en_US

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