4,588 research outputs found

    EyeScout: Active Eye Tracking for Position and Movement Independent Gaze Interaction with Large Public Displays

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    While gaze holds a lot of promise for hands-free interaction with public displays, remote eye trackers with their confined tracking box restrict users to a single stationary position in front of the display. We present EyeScout, an active eye tracking system that combines an eye tracker mounted on a rail system with a computational method to automatically detect and align the tracker with the user's lateral movement. EyeScout addresses key limitations of current gaze-enabled large public displays by offering two novel gaze-interaction modes for a single user: In "Walk then Interact" the user can walk up to an arbitrary position in front of the display and interact, while in "Walk and Interact" the user can interact even while on the move. We report on a user study that shows that EyeScout is well perceived by users, extends a public display's sweet spot into a sweet line, and reduces gaze interaction kick-off time to 3.5 seconds -- a 62% improvement over state of the art solutions. We discuss sample applications that demonstrate how EyeScout can enable position and movement-independent gaze interaction with large public displays

    An automated calibration method for non-see-through head mounted displays

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    Accurate calibration of a head mounted display (HMD) is essential both for research on the visual system and for realistic interaction with virtual objects. Yet, existing calibration methods are time consuming and depend on human judgements, making them error prone, and are often limited to optical see-through HMDs. Building on our existing approach to HMD calibration Gilson et al. (2008), we show here how it is possible to calibrate a non-see-through HMD. A camera is placed inside a HMD displaying an image of a regular grid, which is captured by the camera. The HMD is then removed and the camera, which remains fixed in position, is used to capture images of a tracked calibration object in multiple positions. The centroids of the markers on the calibration object are recovered and their locations re-expressed in relation to the HMD grid. This allows established camera calibration techniques to be used to recover estimates of the HMD display's intrinsic parameters (width, height, focal length) and extrinsic parameters (optic centre and orientation of the principal ray). We calibrated a HMD in this manner and report the magnitude of the errors between real image features and reprojected features. Our calibration method produces low reprojection errors without the need for error-prone human judgements

    A graphical model based solution to the facial feature point tracking problem

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    In this paper a facial feature point tracker that is motivated by applications such as human-computer interfaces and facial expression analysis systems is proposed. The proposed tracker is based on a graphical model framework. The facial features are tracked through video streams by incorporating statistical relations in time as well as spatial relations between feature points. By exploiting the spatial relationships between feature points, the proposed method provides robustness in real-world conditions such as arbitrary head movements and occlusions. A Gabor feature-based occlusion detector is developed and used to handle occlusions. The performance of the proposed tracker has been evaluated on real video data under various conditions including occluded facial gestures and head movements. It is also compared to two popular methods, one based on Kalman filtering exploiting temporal relations, and the other based on active appearance models (AAM). Improvements provided by the proposed approach are demonstrated through both visual displays and quantitative analysis

    3D Gaze Point Localization and Visualization Using LiDAR-based 3D Reconstructions

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    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\u27 problem solving and search task strategies and has high applicability in indoor static environments such as crime scenes

    Investigating eye movement acquisition and analysis technologies as a causal factor in differential prevalence of crossed and uncrossed fixation disparity during reading and dot scanning

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    Previous studies examining binocular coordination during reading have reported conflicting results in terms of the nature of disparity (e.g. Kliegl, Nuthmann, &amp; Engbert (Journal of Experimental Psychology General 135:12-35, 2006); Liversedge, White, Findlay, &amp; Rayner (Vision Research 46:2363-2374, 2006). One potential cause of this inconsistency is differences in acquisition devices and associated analysis technologies. We tested this by directly comparing binocular eye movement recordings made using SR Research EyeLink 1000 and the Fourward Technologies Inc. DPI binocular eye-tracking systems. Participants read sentences or scanned horizontal rows of dot strings; for each participant, half the data were recorded with the EyeLink, and the other half with the DPIs. The viewing conditions in both testing laboratories were set to be very similar. Monocular calibrations were used. The majority of fixations recorded using either system were aligned, although data from the EyeLink system showed greater disparity magnitudes. Critically, for unaligned fixations, the data from both systems showed a majority of uncrossed fixations. These results suggest that variability in previous reports of binocular fixation alignment is attributable to the specific viewing conditions associated with a particular experiment (variables such as luminance and viewing distance), rather than acquisition and analysis software and hardware.<br/

    Ego-Downward and Ambient Video based Person Location Association

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    Using an ego-centric camera to do localization and tracking is highly needed for urban navigation and indoor assistive system when GPS is not available or not accurate enough. The traditional hand-designed feature tracking and estimation approach would fail without visible features. Recently, there are several works exploring to use context features to do localization. However, all of these suffer severe accuracy loss if given no visual context information. To provide a possible solution to this problem, this paper proposes a camera system with both ego-downward and third-static view to perform localization and tracking in a learning approach. Besides, we also proposed a novel action and motion verification model for cross-view verification and localization. We performed comparative experiments based on our collected dataset which considers the same dressing, gender, and background diversity. Results indicate that the proposed model can achieve 18.32%18.32 \% improvement in accuracy performance. Eventually, we tested the model on multi-people scenarios and obtained an average 67.767%67.767 \% accuracy
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