1,297 research outputs found

    Estimating Fusion Weights of a Multi-Camera Eye Tracking System by Leveraging User Calibration Data

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    Cross-ratio (CR)-based eye tracking has been attracting much interest due to its simple setup, yet its accuracy is lower than that of the model-based approaches. In order to improve the estimation accuracy, a multi-camera setup can be exploited rather than the traditional single camera systems. The overall gaze point can be computed by fusion of available gaze information from all cameras. This paper presents a real-time multi-camera eye tracking system in which the estimation of gaze relies on simple CR geometry. A novel weighted fusion method is proposed, which leverages the user calibration data to learn the fusion weights. Experimental results conducted on real data show that the proposed method achieves a significant accuracy improvement over single camera systems. The real-time system achieves 0.82 degrees of visual angle accuracy error with very few calibration data (5 points) under natural head movements, which is competitive with more complex model-based systems

    Robust Eye Tracking Based on Adaptive Fusion of Multiple Cameras

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    Eye and gaze movements play an essential role in identifying individuals' emotional states, cognitive activities, interests, and attention among other behavioral traits. Besides, they are natural, fast, and implicitly reflect the targets of interest, which makes them a highly valuable input modality in human-computer interfaces. Therefore, tracking gaze movements, in other words, eye tracking is of great interest to a large number of disciplines, including human behaviour research, neuroscience, medicine, and human-computer interaction. Tracking gaze movements accurately is a challenging task, especially under unconstrained conditions. Over the last two decades, significant advances have been made in improving the gaze estimation accuracy. However, these improvements have been achieved mostly under controlled settings. Meanwhile, several concerns have arisen, such as the complexity, inflexibility and cost of the setups, increased user effort, and high sensitivity to varying real-world conditions. Despite various attempts and promising enhancements, existing eye tracking systems are still inadequate to overcome most of these concerns, which prevent them from being widely used. In this thesis, we revisit these concerns and introduce a novel multi-camera eye tracking framework. The proposed framework achieves a high estimation accuracy while requiring a minimal user effort and a non-intrusive flexible setup. In addition, it provides improved robustness to large head movements, illumination changes, use of eye wear, and eye type variations across users. We develop a novel real-time gaze estimation framework based on adaptive fusion of multiple single-camera systems, in which the gaze estimation relies on projective geometry. Besides, to ease the user calibration procedure, we investigate several methods to model the subject-specific estimation bias, and consequently, propose a novel approach based on weighted regularized least squares regression. The proposed method provides a better calibration modeling than state-of-the-art methods, particularly when using low-resolution and limited calibration data. Being able to operate with low-resolution data also enables to utilize a large field-of-view setup, so that large head movements are allowed. To address aforementioned robustness concerns, we propose to leverage multiple eye appearances simultaneously acquired from various views. In comparison with conventional single view approach, the main benefit of our approach is to more reliably detect gaze features under challenging conditions, especially when they are obstructed due to large head pose or movements, or eye glasses effects. We further propose an adaptive fusion mechanism to effectively combine the gaze outputs obtained from multi-view appearances. To this effect, our mechanism firstly determines the estimation reliability of each gaze output and then performs a reliability-based weighted fusion to compute the overall point of regard. In addition, to address illumination and eye type robustness, the setup is built upon active illumination and robust feature detection methods are developed. The proposed framework and methods are validated through extensive simulations and user experiments featuring 20 subjects. The results demonstrate that our framework provides not only a significant improvement in gaze estimation accuracy but also a notable robustness to real-world conditions, making it suitable for a large spectrum of applications

    An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices

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    In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.PostprintPeer reviewe

    A Regression-based User Calibration Framework for Real-time Gaze Estimation

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    Eye movements play a very significant role in human computer interaction (HCI) as they are natural and fast, and contain important cues for human cognitive state and visual attention. Over the last two decades, many techniques have been proposed to accurately estimate the gaze. Among these, video-based remote eye trackers have attracted much interest since they enable non-intrusive gaze estimation. To achieve high estimation accuracies for remote systems, user calibration is inevitable in order to compensate for the estimation bias caused by person-specific eye parameters. Although several explicit and implicit user calibration methods have been proposed to ease the calibration burden, the procedure is still cumbersome and needs further improvement. In this paper, we present a comprehensive analysis of regression-based user calibration techniques. We propose a novel weighted least squares regression-based user calibration method together with a real-time cross-ratio based gaze estimation framework. The proposed system enables to obtain high estimation accuracy with minimum user effort which leads to user-friendly HCI applications. Experimental results conducted on both simulations and user experiments show that our framework achieves a significant performance improvement over the state-of-the-art user calibration methods when only a few points are available for the calibration

    Towards High-Frequency Tracking and Fast Edge-Aware Optimization

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    This dissertation advances the state of the art for AR/VR tracking systems by increasing the tracking frequency by orders of magnitude and proposes an efficient algorithm for the problem of edge-aware optimization. AR/VR is a natural way of interacting with computers, where the physical and digital worlds coexist. We are on the cusp of a radical change in how humans perform and interact with computing. Humans are sensitive to small misalignments between the real and the virtual world, and tracking at kilo-Hertz frequencies becomes essential. Current vision-based systems fall short, as their tracking frequency is implicitly limited by the frame-rate of the camera. This thesis presents a prototype system which can track at orders of magnitude higher than the state-of-the-art methods using multiple commodity cameras. The proposed system exploits characteristics of the camera traditionally considered as flaws, namely rolling shutter and radial distortion. The experimental evaluation shows the effectiveness of the method for various degrees of motion. Furthermore, edge-aware optimization is an indispensable tool in the computer vision arsenal for accurate filtering of depth-data and image-based rendering, which is increasingly being used for content creation and geometry processing for AR/VR. As applications increasingly demand higher resolution and speed, there exists a need to develop methods that scale accordingly. This dissertation proposes such an edge-aware optimization framework which is efficient, accurate, and algorithmically scales well, all of which are much desirable traits not found jointly in the state of the art. The experiments show the effectiveness of the framework in a multitude of computer vision tasks such as computational photography and stereo.Comment: PhD thesi

    Optical Gaze Tracking with Spatially-Sparse Single-Pixel Detectors

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    Gaze tracking is an essential component of next generation displays for virtual reality and augmented reality applications. Traditional camera-based gaze trackers used in next generation displays are known to be lacking in one or multiple of the following metrics: power consumption, cost, computational complexity, estimation accuracy, latency, and form-factor. We propose the use of discrete photodiodes and light-emitting diodes (LEDs) as an alternative to traditional camera-based gaze tracking approaches while taking all of these metrics into consideration. We begin by developing a rendering-based simulation framework for understanding the relationship between light sources and a virtual model eyeball. Findings from this framework are used for the placement of LEDs and photodiodes. Our first prototype uses a neural network to obtain an average error rate of 2.67{\deg} at 400Hz while demanding only 16mW. By simplifying the implementation to using only LEDs, duplexed as light transceivers, and more minimal machine learning model, namely a light-weight supervised Gaussian process regression algorithm, we show that our second prototype is capable of an average error rate of 1.57{\deg} at 250 Hz using 800 mW.Comment: 10 pages, 8 figures, published in IEEE International Symposium on Mixed and Augmented Reality (ISMAR) 202

    Neural Radiance Fields: Past, Present, and Future

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    The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation
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