9,341 research outputs found

    Unobtrusive and pervasive video-based eye-gaze tracking

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    Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

    Learning to Personalize in Appearance-Based Gaze Tracking

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    Personal variations severely limit the performance of appearance-based gaze tracking. Adapting to these variations using standard neural network model adaptation methods is difficult. The problems range from overfitting, due to small amounts of training data, to underfitting, due to restrictive model architectures. We tackle these problems by introducing the SPatial Adaptive GaZe Estimator (SPAZE). By modeling personal variations as a low-dimensional latent parameter space, SPAZE provides just enough adaptability to capture the range of personal variations without being prone to overfitting. Calibrating SPAZE for a new person reduces to solving a small optimization problem. SPAZE achieves an error of 2.70 degrees with 9 calibration samples on MPIIGaze, improving on the state-of-the-art by 14 %. We contribute to gaze tracking research by empirically showing that personal variations are well-modeled as a 3-dimensional latent parameter space for each eye. We show that this low-dimensionality is expected by examining model-based approaches to gaze tracking. We also show that accurate head pose-free gaze tracking is possible

    Navigation Assistance in Virtual Worlds

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    Biometric features modeling to measure students engagement.

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    The ability to measure students’ engagement in an educational setting may improve student retention and academic success, revealing which students are disinterested, or which segments of a lesson are causing difficulties. This ability will facilitate timely intervention in both the learning and the teaching process in a variety of classroom settings. In this dissertation, an automatic students engagement measure is proposed through investigating three main engagement components of the engagement: the behavioural engagement, the emotional engagement and the cognitive engagement. The main goal of the proposed technology is to provide the instructors with a tool that could help them estimating both the average class engagement level and the individuals engagement levels while they give the lecture in real-time. Such system could help the instructors to take actions to improve students\u27 engagement. Also, it can be used by the instructor to tailor the presentation of material in class, identify course material that engages and disengages with students, and identify students who are engaged or disengaged and at risk of failure. A biometric sensor network (BSN) is designed to capture data consist of individuals facial capture cameras, wall-mounted cameras and high performance computing machine to capture students head pose, eye gaze, body pose, body movements, and facial expressions. These low level features will be used to train a machine-learning model to estimate the behavioural and emotional engagements in either e-learning or in-class environment. A set of experiments is conducted to compare the proposed technology with the state-of-the-art frameworks in terms of performance. The proposed framework shows better accuracy in estimating both behavioral and emotional engagement. Also, it offers superior flexibility to work in any educational environment. Further, this approach allows quantitative comparison of teaching methods, such as lecture, flipped classrooms, classroom response systems, etc. such that an objective metric can be used for teaching evaluation with immediate closed-loop feedback to the instructor

    Landmark Visualization on Mobile Maps – Effects on Visual Attention, Spatial Learning, and Cognitive Load during Map-Aided Real-World Navigation of Pedestrians

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    Even though they are day-to-day activities, humans find navigation and wayfinding to be cognitively challenging. To facilitate their everyday mobility, humans increasingly rely on ubiquitous mobile maps as navigation aids. However, the over-reliance on and habitual use of omnipresent navigation aids deteriorate humans' short-term ability to learn new information about their surroundings and induces a long-term decline in spatial skills. This deterioration in spatial learning is attributed to the fact that these aids capture users' attention and cause them to enter a passive navigation mode. Another factor that limits spatial learning during map-aided navigation is the lack of salient landmark information on mobile maps. Prior research has already demonstrated that wayfinders rely on landmarks—geographic features that stand out from their surroundings—to facilitate navigation and build a spatial representation of the environments they traverse. Landmarks serve as anchor points and help wayfinders to visually match the spatial information depicted on the mobile map with the information collected during the active exploration of the environment. Considering the acknowledged significance of landmarks for human wayfinding due to their visibility and saliency, this thesis investigates an open research question: how to graphically communicate landmarks on mobile map aids to cue wayfinders' allocation of attentional resources to these task-relevant environmental features. From a cartographic design perspective, landmarks can be depicted on mobile map aids on a graphical continuum ranging from abstract 2D text labels to realistic 3D buildings with high visual fidelity. Based on the importance of landmarks for human wayfinding and the rich cartographic body of research concerning their depiction on mobile maps, this thesis investigated how various landmark visualization styles affect the navigation process of two user groups (expert and general wayfinders) in different navigation use contexts (emergency and general navigation tasks). Specifically, I conducted two real-world map-aided navigation studies to assess the influence of various landmark visualization styles on wayfinders' navigation performance, spatial learning, allocation of visual attention, and cognitive load. In Study I, I investigated how depicting landmarks as abstract 2D building footprints or realistic 3D buildings on the mobile map affected expert wayfinders' navigation performance, visual attention, spatial learning, and cognitive load during an emergency navigation task. I asked expert navigators recruited from the Swiss Armed Forces to follow a predefined route using a mobile map depicting landmarks as either abstract 2D building footprints or realistic 3D buildings and to identify the depicted task-relevant landmarks in the environment. I recorded the experts' gaze behavior with a mobile eye-tracer and their cognitive load with EEG during the navigation task, and I captured their incidental spatial learning at the end of the task. The wayfinding experts' exhibited high navigation performance and low cognitive load during the map-aided navigation task regardless of the landmark visualization style. Their gaze behavior revealed that wayfinding experts navigating with realistic 3D landmarks focused more on the visualizations of landmarks on the mobile map than those who navigated with abstract 2D landmarks, while the latter focused more on the depicted route. Furthermore, when the experts focused for longer on the environment and the landmarks, their spatial learning improved regardless of the landmark visualization style. I also found that the spatial learning of experts with self-reported low spatial abilities improved when they navigated with landmarks depicted as realistic 3D buildings. In Study II, I investigated the influence of abstract and realistic 3D landmark visualization styles on wayfinders sampled from the general population. As in Study I, I investigated wayfinders' navigation performance, visual attention, spatial learning, and cognitive load. In contrast to Study I, the participants in Study II were exposed to both landmark visualization styles in a navigation context that mimics everyday navigation. Furthermore, the participants were informed that their spatial knowledge of the environment would be tested after navigation. As in Study I, the wayfinders in Study II exhibited high navigation performance and low cognitive load regardless of the landmark visualization style. Their visual attention revealed that wayfinders with low spatial abilities and wayfinders familiar with the study area fixated on the environment longer when they navigated with realistic 3D landmarks on the mobile map. Spatial learning improved when wayfinders with low spatial abilities were assisted by realistic 3D landmarks. Also, when wayfinders were assisted by realistic 3D landmarks and paid less attention to the map aid, their spatial learning improved. Taken together, the present real-world navigation studies provide ecologically valid results on the influence of various landmark visualization styles on wayfinders. In particular, the studies demonstrate how visualization style modulates wayfinders' visual attention and facilitates spatial learning across various user groups and navigation use contexts. Furthermore, the results of both studies highlight the importance of individual differences in spatial abilities as predictors of spatial learning during map-assisted navigation. Based on these findings, the present work provides design recommendations for future mobile maps that go beyond the traditional concept of "one fits all." Indeed, the studies support the cause for landmark depiction that directs individual wayfinders' visual attention to task-relevant landmarks to further enhance spatial learning. This would be especially helpful for users with low spatial skills. In doing so, future mobile maps could dynamically adapt the visualization style of landmarks according to wayfinders' spatial abilities for cued visual attention, thus meeting individuals' spatial learning needs

    Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

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    The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop
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