000231152 001__ 231152
000231152 005__ 20190717172529.0
000231152 0247_ $$2doi$$a10.5075/epfl-thesis-7933
000231152 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis7933-5
000231152 02471 $$2nebis$$a11032741
000231152 037__ $$aTHESIS
000231152 041__ $$aeng
000231152 088__ $$a7933
000231152 245__ $$aRobust Eye Tracking Based on Adaptive Fusion of Multiple Cameras
000231152 260__ $$bEPFL$$c2017$$aLausanne
000231152 269__ $$a2017
000231152 300__ $$a167
000231152 336__ $$aTheses
000231152 502__ $$aDr Jean-Michel Sallese (président) ; Prof. Jean-Philippe Thiran (directeur de thèse) ; Dr Jean-Marc Odobez, Dr Dan Witzner Hansen, Dr Hazim Kemal Ekenel (rapporteurs)
000231152 520__ $$aEye 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.
000231152 6531_ $$aeye tracking
000231152 6531_ $$agaze estimation
000231152 6531_ $$amulti-camera eye tracking
000231152 6531_ $$aadaptive fusion
000231152 6531_ $$amulti-camera fusion
000231152 6531_ $$arobust eye tracking
000231152 6531_ $$aconvenient user calibration
000231152 6531_ $$areal-time eye tracking
000231152 6531_ $$acomputer vision
000231152 6531_ $$ahuman-computer interaction
000231152 700__ $$0246687$$g222228$$aArar, Nuri Murat
000231152 720_2 $$aThiran, Jean-Philippe$$edir.$$g115534$$0240323
000231152 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/231152/files/EPFL_TH7933.pdf$$s9256970
000231152 909C0 $$xU10954$$pLTS5$$0252394
000231152 909CO $$pthesis$$pthesis-bn2018$$pthesis-public$$pDOI$$ooai:infoscience.tind.io:231152$$qDOI2$$qGLOBAL_SET$$pSTI
000231152 917Z8 $$x108898
000231152 917Z8 $$x108898
000231152 918__ $$dEDEE$$cIEL$$aSTI
000231152 919__ $$aLTS5
000231152 920__ $$b2017$$a2017-9-29
000231152 970__ $$a7933/THESES
000231152 973__ $$sPUBLISHED$$aEPFL
000231152 980__ $$aTHESIS